Covid19Japan.com

Covid19 Japanが独自に収集している陽性者単位のデータ（個票データ）。ソースとデータは全てGitHubにて公開されており、データはJSON形式。「レコード数 \(\neq\) 累計陽性者数」であることに注意。

　

Import

Covid19 JapanがGitHubで公開しているデータは前述のようにJSON形式であり、最新データはlatest.jsonファイルで示されている。このため、読み込む際はひと工夫必要。

個票データ（Patient Data）

陽性者単位の個票データ。

# path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/patient_data/"
# 
# df <- path %>% 
#   paste0("latest.json") %>% 
#   readr::read_lines() %>% 
#   paste0(path, .) %>% 
#   jsonlite::fromJSON()

df <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/patient_data/latest.json" %>% 
  jsonlite::fromJSON()

df

　

集計データ（Summary Data）

死亡者数や重症者数などの推移データはsummaryフォルダ内のJSON形式ファイルにまとめられている。読み込むと分かるがリスト型で、その中データフレームが含まれる形式である。
summaryフォルダの他にsummary_minフォルダというフォルダがあるが、summary_minフォルダ内のJSONファイルは単に改行を省略して小さくしたファイル。

# path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/summary/"
# 
# df_s <- path %>% 
#   paste0("latest.json") %>% 
#   readr::read_lines() %>% 
#   paste0(path, .) %>% 
#   jsonlite::fromJSON()

df_s <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/summary/latest.json" %>% 
  jsonlite::fromJSON()

df_s %>% summary()

##             Length Class      Mode     
## prefectures 27     data.frame list     
## regions     12     data.frame list     
## daily       37     data.frame list     
## updated      1     -none-     character

　
三つのデータフレームと一つのベクトル（更新日時）から構成されている。データフレームは上から順に都道府県別、地方別、日次となっているが、Lengthを見てわかるようにそれぞれに含まれる集計データが異なっている。

　

都道府県単位集計

更新日時（$updated）における都道府県単位での累積値。厚生労働省がオープンデータから除いている空港検疫・ダイヤモンドプリンセス・長崎クルーズ船・その他が含まれるので全51区分になっている。

df_s$prefectures

陽性者・死亡者などの時系列集計データがネストされて格納されている。日付はネストされていないので、各項目に対するstartDateの項を参照すること。

項目	内容	備考
dailyConfirmedCount	陽性者数	単日
dailyConfirmedStartDate	陽性者数のカウント開始日	区分により開始日が異なる
dailyDeceasedCount	死亡者数	単日
dailyDeceasedStartDate	死亡者数のカウント開始日	区分により開始日が異なる
dailyRecoveredCumulative	快復者数	累計
dailyRecoveredStartDate	快復者数のカウント開始日	区分により開始日が異なる
dailyActive	治療者数1	単日
dailyActiveStartDate	治療者数のカウント開始日	区分により開始日が異なる

¹ 陽性者数から死亡者数と快復者数を引いた数値を治療者数としている

　

地方単位集計

更新日次時点における地方区分単位での累積値。陽性者の時系列集計データが都道府県単位データと同様にネストで格納されているが、死亡者・快復者・治療者のデータは含まれていない。
なお、時系列データの合計値と累積項の値が一致しない場合がある。

df_s$regions

df_s$regions$confirmed[1]

## [1] 93072

df_s$regions$dailyConfirmedCount[[1]] %>% sum()

## [1] 102013

　

日次集計

個票データを日次で集計したもの。日付を見れば分かる通り暗黙の欠落を含んでいる。

df_s$daily

　

更新日時

集計データの更新日時。

df_s$updated

## [1] "2020-12-19T22:35:04+09:00"

　

Area Data

地域・地方ごとの分析を行う場合に便利な都道府県データを用意した。このデータはGistで公開している。

　

Others

病床データ

新型コロナウイルス対策病床オープンデータのデータも用意しておく。

if (googlesheets4::gs4_has_token()) {
beds_by_pref <- "https://docs.google.com/spreadsheets/d/1u0Ul8TgJDqoZMnqFrILyXzTHvuHMht1El7wDZeVrpp8" %>% 
  googlesheets4::read_sheet() %>% 
  dplyr::arrange(dplyr::desc(`発表日`)) %>% 
  dplyr::distinct(`自治体名`, .keep_all = TRUE) %>% 
  dplyr::rename(pref = `自治体名`, beds = `新型コロナウイルス対策感染症病床数`,
                date = `発表日`) %>% 
  dplyr::mutate(beds = as.integer(beds), date = lubridate::as_date(date))
beds_by_pref
}

　

対策ダッシュボード

NECソリューションイノベータによる都道府県単位の単日集計データ。治療に関する集計データが含まれている。ただし、項目によっては累積値（累計値）のものもある。

"https://covid-19.nec-solutioninnovators.com/download/japan_covid19.csv" %>% 
  readr::read_csv(guess_max = 12500) %>% 
  dplyr::select(date = `公表_年月日`, pref = `都道府県名`,
                confirmed = `PCR検査陽性者`, pcr = `PCR検査実施人数`,
                `入院治療等を要する者`, `うち重症`, `退院又は療養解除となった者の数`,
                `確認中`) %>% 
  dplyr::mutate(date = lubridate::as_date(date)) %>% 
  dplyr::mutate_if(is.numeric, .funs = as.integer)

　

新型コロナ関連ニュース

新型コロナ関連のニュース

news <- "https://gist.githubusercontent.com/k-metrics/76fea197fa32466a2f99ff59f721b98a/raw/4c971a6cde2033e458525b793e832c4a87cbae2d/covid19_news.csv" %>% 
    readr::read_csv() %>% 
  dplyr::filter(area == "日本")
news

　

Summarize

最初に個票データの内容を確認する。これには要約に便利なskimrパッケージを用いる。

df %>% 
  skimr::skim()

Data summary

Name	Piped data
Number of rows	199403
Number of columns	23
_______________________
Column type frequency:
character	19
logical	3
numeric	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
patientId	0	1.00	1	8	0	196738	0
dateAnnounced	0	1.00	10	10	0	326	0
gender	88683	0.56	1	1	0	2	0
detectedPrefecture	0	1.00	3	15	0	49	0
patientStatus	194488	0.02	8	23	0	8	0
notes	109639	0.45	1	270	0	86783	1
mhlwPatientNumber	198954	0.00	1	11	0	434	0
prefecturePatientNumber	82143	0.59	5	20	0	117251	0
prefectureSourceURL	168003	0.16	5	224	0	3455	0
residence	96773	0.51	1	38	0	1429	0
sourceURL	1356	0.99	1	239	0	9966	0
relatedPatients	186816	0.06	2	259	0	7470	0
knownCluster	196869	0.01	3	88	0	235	0
detectedCityTown	170370	0.15	2	22	0	667	0
cityPrefectureNumber	170671	0.14	1	34	0	28723	2
citySourceURL	187170	0.06	7	317	0	3695	0
deceasedDate	196647	0.01	10	10	0	276	0
deceasedReportedDate	198179	0.01	10	62	0	208	0
deathSourceURL	198324	0.01	14	123	0	658	0

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
confirmedPatient	0	1	0.99	TRU: 196737, FAL: 2666
charterFlightPassenger	199389	0	1.00	TRU: 14
cruisePassengerDisembarked	199392	0	1.00	TRU: 11

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
ageBracket	0	1	20.64	24.77	-1	-1	20	40	100	▇▃▂▂▁

　
元がJSON形式なので、読み込んだ直後は殆どの変量（フィーチャー）が文字型になっていることが分かる。また、意外と欠損が多いことも分かる。

　

Tidy & Transform

各変量（フィーチャー）を適切な形式に変換し、地域区分でも分析できるように都道府県データと結合することで、ベースとなるデータセットを作成する。なお、都道府県以外で報告されたレコードを除いている。

x <- df %>% 
  dplyr::select(patientId, date = dateAnnounced, gender,
                pref = detectedPrefecture, patientStatus, knownCluster,
                confirmedPatient, charterFlightPassenger,
                cruisePassengerDisembarked, ageBracket,
                deceasedDate, deceasedReportedDate) %>% 
  dplyr::filter(confirmedPatient == TRUE) %>% 
  dplyr::mutate(date = lubridate::as_date(date),
                gender = forcats::as_factor(gender),
                patientStatus = forcats::as_factor(patientStatus),
                cluster = dplyr::if_else(!is.na(knownCluster), TRUE, FALSE),
                ageBracket = forcats::as_factor(ageBracket),
                deceasedDate = lubridate::as_date(deceasedDate),
                deceasedReportedDate = lubridate::as_date(deceasedReportedDate)) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  dplyr::select(-`推計人口`, -pref) %>% 
  dplyr::rename(pref = `都道府県`, region = `八地方区分`) %>% 
  tidyr::drop_na(pref)

x

変換結果を要約してみると

x %>% 
  skimr::skim()

Data summary

Name	Piped data
Number of rows	194996
Number of columns	18
_______________________
Column type frequency:
character	2
Date	3
factor	9
logical	4
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
patientId	0	1.00	2	8	0	194996	0
knownCluster	192512	0.01	3	88	0	232	0

Variable type: Date

skim_variable	n_missing	complete_rate	min	max	median	n_unique
date	0	1	2020-01-15	2020-12-19	2020-10-28	323
deceasedDate	194617	0	2020-02-13	2020-11-19	2020-05-08	150
deceasedReportedDate	194667	0	2020-02-13	2020-10-17	2020-05-16	130

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
gender	85881	0.56	FALSE	2	M: 61012, F: 48103
patientStatus	192485	0.01	FALSE	8	Hos: 1246, Dec: 371, Hom: 315, Dis: 276
ageBracket	0	1.00	FALSE	13	-1: 85966, 20: 29297, 30: 18917, 40: 15988
pcode	0	1.00	FALSE	47	13: 50939, 27: 26788, 14: 16644, 23: 13984
pref	0	1.00	FALSE	47	東京都: 50939, 大阪府: 26788, 神奈川: 16644, 愛知県: 13984
region	0	1.00	FALSE	8	関東地: 93072, 近畿地: 42853, 中部地: 21792, 九州地: 16541
広域圏	17007	0.91	FALSE	8	首都圏: 93556, 近畿圏: 41723, 中部圏: 20219, 九州圏: 11585
通俗的区分	0	1.00	FALSE	11	関東: 93072, 関西: 41723, 東海: 19187, 北海道: 12051
fct_pref	0	1.00	FALSE	47	Tok: 50939, Osa: 26788, Kan: 16644, Aic: 13984

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
confirmedPatient	0	1	1.00	TRU: 194996
charterFlightPassenger	194989	0	1.00	TRU: 7
cruisePassengerDisembarked	194985	0	1.00	TRU: 11
cluster	0	1	0.01	FAL: 192512, TRU: 2484

　
文字型を因子型に変換するだけでも大まかな傾向が見えるようになる。例えば

• 年齢別で見ると20代、30代、年齢不明（恐らく非回答）、40代の順に多い
• 都道府県別では東京、大阪、神奈川、愛知の順と人口にほぼ比例
• 地方区分で見ると関東、近畿、九州、中部となっており九州地方が以外と多い

ことが読める。

patientStatusは以下の通りで、ほぼ更新されていないのと思われる。死者数などの推移を見る場合は集計データを使った方がいいことが分かる。

x %>% 
  dplyr::group_by(patientStatus) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(Japanese = c("回復", "入院中", "退院済", "死亡", "詳細不明",
                             "重症", "自宅療養", "ホテル療養", NA))

　

Data Wrangling

陽性者の集計

最初に陽性者をキーに集計する。 　

全国集計

全国の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。

r_by_all <- x %>% 
  dplyr::filter(!is.na(pref)) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::bind_cols(prefs %>% dplyr::summarise(population = sum(`推計人口`))) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_all %>% 
  dplyr::rename(`累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate)

　

地方別集計

次に地方別の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。

region <- prefs %>% 
  dplyr::group_by(`八地方区分`) %>% 
  dplyr::summarise(population = sum(`推計人口`)) %>% 
  dplyr::rename(region = `八地方区分`)

r_by_region <- x %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  dplyr::left_join(region, by = c("region" = "region")) %>% 
  dplyr::select(region, n, population) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_region %>% 
  dplyr::rename(`地方` = region,
                `累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate)

　

上表を可視化する。グレーの破線は切片ゼロで傾きが全国の人口千人あたりの累計陽性者数（1.54）。

r_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

都道府県別集計

同様に都道府県別の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。任意の列でソートできるようにしてある。

r_by_pref <- x %>% 
  dplyr::group_by(pref) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  dplyr::left_join(prefs, by = c("pref" = "都道府県")) %>% 
  dplyr::select(pref, n, population = `推計人口`) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_pref %>% 
  dplyr::rename(`都道府県` = pref,
                `累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate) %>% 
  tibble::rowid_to_column("No") %>% 
  DT::datatable()

　

上表を可視化する。グレーの破線は切片ゼロで傾きが全国の人口千人あたりの累計陽性者数（1.54）。

r_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

推計人口が550万人未満の都道府県のみ抽出する。グレーの破線は上図と同様。

r_by_pref %>% 
  dplyr::filter(population < 5500) %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

クラスタ比率

全国のクラスタ比率

x %>% 
  dplyr::filter(!is.na(pref)) %>% 
  dplyr::group_by(cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  dplyr::rename(`非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio)

地方別クラスタ比率

地方別の累計陽性者数の内、クラスタ感染と判定された人数の割合を求める。

x %>% 
  dplyr::group_by(region, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  dplyr::rename(`地方` = region,
                `非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio)

　

都道府県別クラスタ比率

同様に都道府県別のクラスタ比率。任意の列でソートできるようにしてある。

x %>% 
  dplyr::group_by(pref, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  tidyr::replace_na(list(`TRUE` = 0L, ratio = 0.0)) %>% 
  dplyr::rename(`都道府県` = pref,
                `非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio) %>% 
  tibble::rowid_to_column(var = "No") %>% 
  DT::datatable()

　

陽性者の日次集計

　

全国日次集計

全国の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_all <- x %>% 
  dplyr::group_by(date) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day"),
                  fill = list(n = 0L)) %>% 
  dplyr::mutate(diff = lagdiff(n), cum = cumsum(n), ma7 = ma7(n), ma28 = ma28(n))

x_by_all %>% 
  dplyr::select(`発表日` = date, `陽性者数` = n, `前日差` = diff,
                `累計陽性者数` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

# 祝日ファイルは以下をダウンロードしておく（SSLがエラーになるので自動処理できない）
# "https://www8.cao.go.jp/chosei/shukujitsu/syukujitsu.csv"

# 祝日判定関数
source("https://raw.githubusercontent.com/logics-of-blue/website/master/010_forecast/20190714_R%E8%A8%80%E8%AA%9E%E3%81%AB%E3%81%8A%E3%81%91%E3%82%8B%E6%97%A5%E6%9C%AC%E3%81%AE%E7%A5%9D%E6%97%A5%E5%88%A4%E5%AE%9A/jholiday.R", encoding="utf-8")

sec_scale <- 100

emergency <- news %>% 
  dplyr::filter(category == "緊急事態")

goto <- news %>% 
  dplyr::filter(category == "GoTo")

x_by_all %>% 
  dplyr::mutate(hday = is.jholiday(target_date = date,
                                   holiday_source = "./Covid19/syukujitsu.csv"),
                wday = lubridate::wday(date, week_start = 1),
                wday = dplyr::if_else(wday > 5, TRUE, FALSE),
                wday = dplyr::if_else(hday | wday, 3000L,  NA_integer_)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = wday), stat = "identity", width = 1.0,
                      fill = "red", alpha = 0.1) + 
    ggplot2::geom_vline(ggplot2::aes(xintercept = date), data = emergency,
                        colour = "dark blue", linetype = "dashed", size = 0.15) +
    ggplot2::geom_vline(ggplot2::aes(xintercept = date), data = goto,
                        colour = "magenta", linetype = "dashed", size = 0.25) + 
    ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
                      fill = "dark gray", alpha = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
                       colour = "dark green", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
                       colour = "dark green", size = 1.0) +
    ggplot2::labs(title = paste0("【全国】陽性者数の推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") +
    ggrepel::geom_label_repel(ggplot2::aes(x = date, y = 2500, label = news),
                              size = 2.0, data = emergency) +
    ggrepel::geom_label_repel(ggplot2::aes(x = date, y = 2250, label = news),
                              size = 2.5, data = goto, colour = "magenta") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) + 
    ggplot2::labs(title = paste0("【全国】陽性者数の前日差 @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "前日差")

　

地方別日次集計

同様に地方別の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_region <- x %>% 
  dplyr::group_by(date, region) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  tidyr::pivot_wider(names_from = region, values_from = n, values_fill = 0L) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>% 
  tidyr::pivot_longer(cols = -date, names_to = "region", values_to = "n") %>% 
  tidyr::replace_na(replace = list(n = 0L)) %>% 
  dplyr::group_by(region) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n)),
                ma28 = purrr::map(data, ~ ma28(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs %>% dplyr::distinct(`八地方区分`), .,
                   by = c("八地方区分" = "region")) %>% 
  dplyr::mutate(region = forcats::fct_inorder(`八地方区分`)) %>% 
  dplyr::arrange(date)

x_by_region %>% 
  dplyr::filter(date == max(date)) %>% 
  dplyr::mutate(ma7 = round(ma7, 1)) %>% 
  dplyr::select(`地方` = region,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

x_by_region %>% 
  dplyr::select(`地方` = region,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = n)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      width = 1.0, alpha = 0.5) + 
    ggplot2::labs(title = paste0("【地方別】陽性者数の推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "陽性者数") 

　

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = ma7, colour = region)) + 
    ggplot2::geom_line(size = 1) +
    ggplot2::theme(legend.position = 'none') +
    ggplot2::labs(title = paste0("【地方別】移動平均（7日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "陽性者数") + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region),
                             data = subset(x_by_region, date == max(date)),
                             nudge_x = 30, segment.alpha = 0.5, size = 4) + 
    ggplot2::lims(x = c(min(x_by_region$date),
                        max(x_by_region$date) + 45))

　

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = cum, colour = region)) + 
    ggplot2::geom_line(size = 1) +
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("【地方別】累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "累計陽性者数") + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region),
                             data = subset(x_by_region, date == max(date)),
                             nudge_x = 30, segment.alpha = 0.5, size = 4) + 
    ggplot2::lims(x = c(min(x_by_region$date),
                        max(x_by_region$date) + 45))

　

地方単位で可視化。

sec_scale <- 20
ncol <- 2

x_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.5, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "dotted", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（点線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "陽性者累計（実線）")
    )

　

傾向が見えるように縦軸をフリースケールとする。

sec_scale <- 20
ncol <- 2

x_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.5, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "dashed", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol  = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "陽性者累計（実線）")
    )

　

都道府県別日次集計

同様に都道府県別の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_pref <- x %>% 
  dplyr::group_by(date, pref) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  tidyr::pivot_wider(names_from = pref, values_from = n, values_fill = 0L) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>% 
  tidyr::pivot_longer(cols = -date, names_to = "pref", values_to = "n") %>% 
  tidyr::replace_na(replace = list(n = 0L)) %>% 
  dplyr::group_by(pref) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n)),
                ma28 = purrr::map(data, ~ ma28(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs, ., by = c("都道府県" = "pref")) %>% 
  dplyr::mutate(pref = forcats::fct_inorder(`都道府県`)) %>% 
  dplyr::arrange(date)

x_by_pref %>% 
  dplyr::filter(date == max(date)) %>% 
  dplyr::mutate(ma7 = round(ma7, 1)) %>% 
  dplyr::select(`都道府県` = pref,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7) %>% 
  DT::datatable()

x_by_pref %>% 
  dplyr::select(`都道府県` = pref,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

x_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計陽性者数（折線）")
    )

　

傾向が見えるように縦軸をフリースケールとする。

x_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計陽性者数（折線）")
    )

　

死亡者の日次集計

厚生労働省のデータと乖離がある。 　

都道府県別

都道府県別の日次単位の死亡者数、前日差、累計、移動平均（7日）を求める。

start <- df_s$prefectures %>% 
  dplyr::select(pref = name, date = dailyDeceasedStartDate) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  dplyr::arrange(pcode) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::select(date, pref = `都道府県`) %>% 
  dplyr::distinct(date) %>% 
  .$date %>% lubridate::as_date()

d_by_prefs <- df_s$prefectures %>% 
  dplyr::select(deceased = dailyDeceasedCount, pref = name) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::select(pref = `都道府県`, deceased) %>% 
  tidyr::unnest(deceased) %>% 
  tidyr::pivot_wider(names_from = pref, values_from = deceased) %>% 
  tidyr::unnest() %>% 
  dplyr::mutate(date = seq.Date(from = start, to = start + nrow(.) - 1,
                                by = "day")) %>% 
  dplyr::select(date, dplyr::everything()) %>% 
  tidyr::pivot_longer(col = -date, names_to = "pref", values_to = "n") %>% 
  dplyr::group_by(pref) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs, ., by = c("都道府県" = "pref")) %>% 
  dplyr::mutate(pref = forcats::fct_inorder(`都道府県`)) %>% 
  dplyr::select(date, pref, n, diff, cum, ma7) %>% 
  dplyr::arrange(date)
d_by_prefs

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

d_by_prefs %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
   ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計陽性者数（折線）")
    )

　

地方別

集計データ$regionsには死亡者数の日次データが存在しないため$prefecturesのデータから計算する。

d_by_region <- d_by_prefs %>% 
  dplyr::select(date, pref = pref, n) %>% 
  dplyr::left_join(prefs, by = c("pref" = "都道府県")) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::group_by(date, `八地方区分`) %>% 
  dplyr::summarise(n = sum(n)) %>% 
  dplyr::ungroup() %>% 
  dplyr::rename(region = `八地方区分`) %>% 
  dplyr::group_by(region) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::arrange(date)
d_by_region

　

陽性者比率と死亡者比率

rpd_by_all <- d_by_region %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_region, ., by = c("region")) %>% 
  dplyr::select(region, positive = n, deceased = d, population) %>% 
  dplyr::select(-region) %>% 
  dplyr::summarise_all(sum) %>% 
  dplyr::mutate(p_rate = round(positive / population, 2),
                d_rate = round(deceased / positive, 2))

rpd_by_all %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

rpd_by_region <- d_by_region %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_region, ., by = c("region")) %>% 
  dplyr::select(region, positive = n, deceased = d, population, p_rate = rate) %>% 
  dplyr::mutate(d_rate = round(deceased / positive, 2))

rpd_by_region %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

rpd_by_prefs <- d_by_prefs %>% 
  dplyr::group_by(pref) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_pref, ., by = "pref") %>% 
  dplyr::select(pref, positive = n, deceased = d, population, p_rate = rate) %>% 
  dplyr::mutate(d_rate = round(deceased / positive, 2)) 

rpd_by_prefs %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

全国日次集計

都道府県別のデータから全国の日次集計を求める。

d_by_all <- d_by_prefs %>% 
  dplyr::group_by(date) %>% 
  dplyr::summarise(n = sum(n)) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(diff = lagdiff(n), cum = cumsum(n), ma7 = ma7(n))
d_by_all

sec_scale <- 50
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
   ggplot2::geom_bar(ggplot2::aes(y = n), fill = "dark gray", stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), colour = "dark green",
                       linetype = "dashed", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale), colour = "dark green") +
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・同移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計死亡者数（実線）")
    )

　

年代別

x_by_age <- x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0-9"))
x_by_age

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>%
  dplyr::group_by(ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(rate = round((n / sum(n) * 100), 1))

　

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>%
  dplyr::group_by(region, ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::pivot_wider(names_from = ageBracket, values_from = n)

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0")) %>%
  dplyr::group_by(region, ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(region = forcats::fct_rev(region)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = region)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() +
    ggplot2::labs(title = paste0("年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0")) %>%
  dplyr::filter(ageBracket != "Unknown") %>% 
  dplyr::group_by(region, ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(region = forcats::fct_rev(region)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = region)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) +
    ggplot2::coord_flip() + 
    ggplot2::labs(title = paste0("年齢不明者をのぞく年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0")) %>%
  dplyr::group_by(region, ageBracket, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(region = forcats::fct_rev(region)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = region)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::facet_grid(~ cluster) +
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() + 
    ggplot2::labs(title = paste0("非クラスタ/クラスタでの年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0")) %>%
  dplyr::filter(ageBracket != "Unknown") %>% 
  dplyr::group_by(region, ageBracket, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(region = forcats::fct_rev(region)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = region)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::facet_grid(~ cluster) +
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() + 
    ggplot2::labs(title = paste0("年齢不明者をのぞく非クラスタ/クラスタでの年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

都道府県別

g_age_by_pref <- x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0-9` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0-9")) %>%
  dplyr::group_by(pref, ageBracket, cluster) %>%
  # dplyr::group_by(pref, ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(pref = forcats::fct_rev(pref)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = pref)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") +
    # ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity") + 
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() + 
    ggplot2::geom_hline(yintercept = c(0.25, 0.5, 0.75), size = 0.35,
                        colour = "dark gray") + 
    ggplot2::labs(title = paste0("年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

g_age_by_pref

g_age_by_pref + 
  ggplot2::facet_grid(~ cluster)

g_age_by_pref_wo <- x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0-9` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0-9")) %>%
  dplyr::filter(ageBracket != "Unknown") %>% 
  dplyr::group_by(pref, ageBracket, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(pref = forcats::fct_rev(pref)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = pref)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() + 
    ggplot2::geom_hline(yintercept = c(0.25, 0.5, 0.75), size = 0.35,
                        colour = "dark gray") + 
    ggplot2::labs(title = paste0("年齢不明者をのぞく年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

g_age_by_pref_wo

g_age_by_pref_wo + 
  ggplot2::facet_grid(~ cluster) + 
  ggplot2::labs(title = paste0("年齢不明者をのぞく非クラスタ/クラスタでの年代構成比 @", datetime))

　

Visualize

前日差

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff, colour = region)) +
    ggplot2::facet_wrap(~ region, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
                  caption = caption, x = "", y = "")

　

都道府県別

　

単日＋累計

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

　

前日差

x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
                  x = "", y = "")

　

死亡者の日次推移

　

全国

sec_scale <- 100
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
                      alpha = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
                       colour = "dark green", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
                       colour = "dark green", size = 1.0) +
    ggplot2::labs(title = paste0("全国の死亡者数推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") +
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) + 
    ggplot2::labs(title = paste0("全国の死亡者数前日差 @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "前日差")

　

地方別

sec_scale <- 50
ncol <- 4
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


d_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.2) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.2) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

　

都道府県別日次推移

sec_scale <- 10
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


d_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数（単日）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数（単日）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

　

比較

陽性者数と死亡者の比較。

　

全国

sec_scale <- (1 / 50)

x_by_all %>% 
  dplyr::left_join(d_by_all, by = c("date")) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n.x), stat = "identity",
                      fill = "dark green", alpha = 0.25, width = 1.0) +
    ggplot2::geom_bar(ggplot2::aes(y = n.y / sec_scale), stat = "identity",
                      fill = "dark red", alpha = 0.25, width = 1.0) +
    # ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") + 
    # ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") + 
    ggplot2::labs(title = paste0("@", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数（濃緑）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "死亡者数（濃赤）")
    )

　

地方別

sec_scale <- (1 / 10)
ncol <- 4

x_by_region %>% 
  dplyr::left_join(d_by_region, by = c("date" = "date", "region" = "region")) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n.x), stat = "identity",
                      fill = "dark green", alpha = 0.25, width = 1.0) +
    ggplot2::geom_bar(ggplot2::aes(y = n.y / sec_scale), stat = "identity",
                      fill = "dark red", alpha = 0.25, width = 1.0) +
    # ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") + 
    # ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") + 
    ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数（濃緑）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "死亡者数（濃赤）")
    )

　

相関

　

地方区分別

r_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = region)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

rpd_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_point(ggplot2::aes(colour = region)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

都道府県別

r_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("@", datetime), caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

r_by_pref %>% 
  dplyr::filter(n < 5000) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("累計陽性者数五千人未満 @", datetime),
                  caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

rpd_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_abline(slope = rpd_by_all$d_rate, intercept = 0, colour = "gray") +
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

rpd_by_prefs %>% 
  dplyr::filter(positive < 1000) %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_abline(slope = rpd_by_all$d_rate, intercept = 0, colour = "gray") +
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

Model

時系列（TS）分析

日本の時系列データは週単位の変動が認められるので、frequencyを7に設定して陽性者数のデータをtsオブジェクトに変換する。

ts_week <- x_by_all %>% 
  dplyr::select(n) %>% 
  ts(frequency = 7)

時系列データに変換したものをプロットすると可視化の項でプロットした棒グラフと同じような形のグラフになることが分かります。

ts_week %>% 
  plot(main = paste0("全国 @", datetime))

上記からトレンド（長期的傾向）を除いたグラフ。デフォルト指定なのでlag = 1。つまり、前日差。

ts_week %>% 
    base::diff() %>% 
  plot(main = paste0("全国 @", datetime))

トレンド、季節変動（周期変動）、非周期変動に分解した場合。frequency = 1では分解できない点に注意。

ts_week %>% 
  stats::decompose() %>% 
  plot()

トレンドを抜き出してみる。移動平均に酷似している。

ymax <- 3000

ts_week %>% 
  stats::decompose() %>% 
  .$x %>% 
  plot(ylim = c(0, ymax), main = paste0("全国 @", datetime))

par(new = TRUE)

ts_week %>% 
  stats::decompose() %>% 
  .$trend %>% 
  plot(ylim = c(0, ymax), col = "dark green", lwd = 3)

　

地方別時系列分析

x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道地方
## NULL
## 
## $東北地方
## NULL
## 
## $関東地方
## NULL
## 
## $中部地方
## NULL
## 
## $近畿地方
## NULL
## 
## $中国地方
## NULL
## 
## $四国地方
## NULL
## 
## $九州地方
## NULL

oldpar <- par()
par(mfrow=c(4, 2))
x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, .y) {
                plot(.x, main = .y, ylim = c(0, max(.x)), col = "dark gray")
                par(new = TRUE)
                stats::decompose(.x) %>% 
                  .$trend %>% 
                  plot(ylim = c(0, max(.x)), col = "dark green", lwd = 2)
              } )

## $北海道地方
## NULL
## 
## $東北地方
## NULL
## 
## $関東地方
## NULL
## 
## $中部地方
## NULL
## 
## $近畿地方
## NULL
## 
## $中国地方
## NULL
## 
## $四国地方
## NULL
## 
## $九州地方
## NULL

par(oldpar)

x_by_pref %>% 
  dplyr::select(pref, n) %>% 
  split(.$pref) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, .y) {
                plot(.x, main = .y, ylim = c(0, max(.x)), col = "dark gray")
                # plot(.x, main = region)
                par(new = TRUE)
                stats::decompose(.x) %>% 
                  .$trend %>% 
                  plot(ylim = c(0, max(.x)), col = "dark green", lwd = 2)
              } )

## $北海道
## NULL
## 
## $青森県
## NULL
## 
## $岩手県
## NULL
## 
## $宮城県
## NULL
## 
## $秋田県
## NULL
## 
## $山形県
## NULL
## 
## $福島県
## NULL
## 
## $茨城県
## NULL
## 
## $栃木県
## NULL
## 
## $群馬県
## NULL
## 
## $埼玉県
## NULL
## 
## $千葉県
## NULL
## 
## $東京都
## NULL
## 
## $神奈川県
## NULL
## 
## $新潟県
## NULL
## 
## $富山県
## NULL
## 
## $石川県
## NULL
## 
## $福井県
## NULL
## 
## $山梨県
## NULL
## 
## $長野県
## NULL
## 
## $岐阜県
## NULL
## 
## $静岡県
## NULL
## 
## $愛知県
## NULL
## 
## $三重県
## NULL
## 
## $滋賀県
## NULL
## 
## $京都府
## NULL
## 
## $大阪府
## NULL
## 
## $兵庫県
## NULL
## 
## $奈良県
## NULL
## 
## $和歌山県
## NULL
## 
## $鳥取県
## NULL
## 
## $島根県
## NULL
## 
## $岡山県
## NULL
## 
## $広島県
## NULL
## 
## $山口県
## NULL
## 
## $徳島県
## NULL
## 
## $香川県
## NULL
## 
## $愛媛県
## NULL
## 
## $高知県
## NULL
## 
## $福岡県
## NULL
## 
## $佐賀県
## NULL
## 
## $長崎県
## NULL
## 
## $熊本県
## NULL
## 
## $大分県
## NULL
## 
## $宮崎県
## NULL
## 
## $鹿児島県
## NULL
## 
## $沖縄県
## NULL

　

Infer

時系列予測（ARIMA）

ARIMA（Auto Regressive Integrated Moving Average, 自己回帰和分移動平均）モデルによる陽性者に対する予測。予測に必要なパラメータはステップワイズにより自動的に最適なものが選択される。ただし、モデル自体を評価していないので、こういうことが出来る程度の話。

　

全国

x_by_all %>% 
  dplyr::select(n) %>% 
  ts(.$n, frequency = 7) %>% 
  forecast::auto.arima() %>%  
  forecast::forecast() %>% 
  plot(main = paste0("全国 @", datetime))

　

地方別

x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map(., forecast::auto.arima) %>% 
  purrr::map(., forecast::forecast) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道地方
## $北海道地方$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 116.50159  91.62504 100.07984  84.00151 114.85162 111.11786 108.61932
##  [8]  93.73385  73.04636  81.27404  70.89493  92.28742  88.38999  88.19557
## 
## $北海道地方$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##                80%       95%
## 49.57143 101.05995 92.885640
## 49.71429  73.41051 63.768328
## 49.85714  76.95766 64.717520
## 50.00000  59.59954 46.681915
## 50.14286  87.83581 73.534502
## 50.28571  81.50558 65.829785
## 50.42857  76.77355 59.915421
## 50.57143  57.88614 38.909508
## 50.71429  33.94866 13.251595
## 50.85714  37.99801 15.089058
## 51.00000  25.23065  1.057440
## 51.14286  44.10753 18.602632
## 51.28571  37.36383 10.352221
## 51.42857  34.37464  5.883556
## 
## $北海道地方$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 131.9432 140.1175
## 49.71429 109.8396 119.4817
## 49.85714 123.2020 135.4422
## 50.00000 108.4035 121.3211
## 50.14286 141.8674 156.1687
## 50.28571 140.7301 156.4059
## 50.42857 140.4651 157.3232
## 50.57143 129.5816 148.5582
## 50.71429 112.1440 132.8411
## 50.85714 124.5501 147.4590
## 51.00000 116.5592 140.7324
## 51.14286 140.4673 165.9722
## 51.28571 139.4161 166.4278
## 51.42857 142.0165 170.5076
## 
## 
## $東北地方
## $東北地方$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 72.71379 69.88646 70.09354 70.30063 70.50772 70.71481 70.92190 71.12898
##  [9] 71.33607 71.54316 71.75025 71.95734 72.16442 72.37151
## 
## $東北地方$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 62.47080 57.04849
## 49.71429 58.68241 52.75135
## 49.85714 58.57320 52.47469
## 50.00000 58.47243 52.21097
## 50.14286 58.37948 51.95918
## 50.28571 58.29378 51.71848
## 50.42857 58.21482 51.48810
## 50.57143 58.14216 51.26735
## 50.71429 58.07540 51.05562
## 50.85714 58.01418 50.85237
## 51.00000 57.95818 50.65710
## 51.14286 57.90710 50.46936
## 51.28571 57.86069 50.28875
## 51.42857 57.81869 50.11488
## 
## $東北地方$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 82.95678 88.37909
## 49.71429 81.09050 87.02156
## 49.85714 81.61389 87.71239
## 50.00000 82.12883 88.39030
## 50.14286 82.63596 89.05626
## 50.28571 83.13584 89.71113
## 50.42857 83.62897 90.35569
## 50.57143 84.11581 90.99062
## 50.71429 84.59675 91.61652
## 50.85714 85.07214 92.23395
## 51.00000 85.54232 92.84340
## 51.14286 86.00757 93.44531
## 51.28571 86.46816 94.04010
## 51.42857 86.92434 94.62814
## 
## 
## $関東地方
## $関東地方$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 1183.628  971.314 1218.115 1540.183 1669.889 1604.582 1653.551 1374.224
##  [9] 1139.941 1381.097 1692.739 1818.510 1756.665 1803.427
## 
## $関東地方$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##                80%       95%
## 49.57143 1105.0457 1063.4466
## 49.71429  871.4193  818.5382
## 49.85714 1109.9637 1052.7117
## 50.00000 1428.7604 1369.7767
## 50.14286 1554.2593 1493.0486
## 50.28571 1486.2891 1423.6685
## 50.42857 1528.9892 1463.0503
## 50.57143 1232.9037 1158.0934
## 50.71429  982.3770  898.9676
## 50.85714 1212.3249 1122.9822
## 51.00000 1514.9759 1420.8737
## 51.14286 1631.6448 1532.7242
## 51.28571 1562.8550 1460.2582
## 51.42857 1600.8049 1493.5431
## 
## $関東地方$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 1262.210 1303.810
## 49.71429 1071.209 1124.090
## 49.85714 1326.267 1383.519
## 50.00000 1651.606 1710.589
## 50.14286 1785.519 1846.729
## 50.28571 1722.875 1785.496
## 50.42857 1778.112 1844.051
## 50.57143 1515.544 1590.354
## 50.71429 1297.505 1380.915
## 50.85714 1549.870 1639.212
## 51.00000 1870.502 1964.605
## 51.14286 2005.376 2104.296
## 51.28571 1950.475 2053.072
## 51.42857 2006.050 2113.312
## 
## 
## $中部地方
## $中部地方$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 268.5497 193.0890 291.9209 341.7634 343.8500 321.9012 337.1057 265.3491
##  [9] 195.5357 288.1321 340.8394 344.7772 320.4469 338.1476
## 
## $中部地方$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 243.0997 229.6273
## 49.71429 162.5111 146.3242
## 49.85714 256.7319 238.1040
## 50.00000 303.4203 283.1227
## 50.14286 302.4339 280.5096
## 50.28571 278.2834 255.1935
## 50.42857 291.2733 267.0111
## 50.57143 212.6786 184.7965
## 50.71429 138.5509 108.3849
## 50.85714 227.6085 195.5693
## 51.00000 277.2370 243.5678
## 51.14286 278.5870 243.5480
## 51.28571 251.9643 215.7118
## 51.42857 267.7122 230.4260
## 
## $中部地方$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 293.9996 307.4720
## 49.71429 223.6669 239.8538
## 49.85714 327.1099 345.7378
## 50.00000 380.1065 400.4041
## 50.14286 385.2661 407.1905
## 50.28571 365.5191 388.6090
## 50.42857 382.9381 407.2004
## 50.57143 318.0196 345.9017
## 50.71429 252.5205 282.6864
## 50.85714 348.6558 380.6950
## 51.00000 404.4419 438.1111
## 51.14286 410.9674 446.0064
## 51.28571 388.9295 425.1820
## 51.42857 408.5830 445.8692
## 
## 
## $近畿地方
## $近畿地方$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 488.8747 359.5531 518.0232 631.1132 608.5680 546.5332 583.3855 494.1334
##  [9] 363.9024 511.8588 613.5555 597.2683 545.3890 580.5172
## 
## $近畿地方$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 440.8830 415.4777
## 49.71429 302.7053 272.6118
## 49.85714 459.0385 427.8139
## 50.00000 569.1378 536.3299
## 50.14286 543.7396 509.4215
## 50.28571 478.9722 443.2075
## 50.42857 513.1982 476.0433
## 50.57143 411.4789 367.7243
## 50.71429 274.2753 226.8295
## 50.85714 418.2256 368.6592
## 51.00000 515.5985 463.7432
## 51.14286 495.1703 441.1229
## 51.28571 439.3116 383.1577
## 51.42857 470.6044 412.4200
## 
## $近畿地方$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 536.8664 562.2717
## 49.71429 416.4009 446.4944
## 49.85714 577.0080 608.2326
## 50.00000 693.0887 725.8966
## 50.14286 673.3965 707.7146
## 50.28571 614.0942 649.8589
## 50.42857 653.5728 690.7278
## 50.57143 576.7878 620.5424
## 50.71429 453.5295 500.9753
## 50.85714 605.4919 655.0583
## 51.00000 711.5126 763.3679
## 51.14286 699.3662 753.4137
## 51.28571 651.4664 707.6204
## 51.42857 690.4300 748.6144
## 
## 
## $中国地方
## $中国地方$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 160.0293 151.6769 169.3153 169.5521 173.2823 181.8687 184.4254 189.9516
##  [9] 195.6449 199.8533 205.3145 210.3690 215.1994 220.4076
## 
## $中国地方$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 148.3447 142.1593
## 49.71429 138.9764 132.2531
## 49.85714 155.9809 148.9220
## 50.00000 154.9885 147.2790
## 50.14286 158.1639 150.1606
## 50.28571 165.7466 157.2120
## 50.42857 167.0714 157.8847
## 50.57143 171.5225 161.7667
## 50.71429 175.9671 165.5503
## 50.85714 178.9470 167.8799
## 51.00000 183.1914 171.4801
## 51.14286 186.9431 174.5423
## 51.28571 190.4557 177.3572
## 51.42857 194.3090 180.4933
## 
## $中国地方$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 171.7138 177.8992
## 49.71429 164.3775 171.1008
## 49.85714 182.6498 189.7086
## 50.00000 184.1158 191.8253
## 50.14286 188.4008 196.4040
## 50.28571 197.9908 206.5253
## 50.42857 201.7794 210.9661
## 50.57143 208.3808 218.1366
## 50.71429 215.3228 225.7396
## 50.85714 220.7596 231.8268
## 51.00000 227.4376 239.1489
## 51.14286 233.7948 246.1957
## 51.28571 239.9431 253.0417
## 51.42857 246.5061 260.3219
## 
## 
## $四国地方
## $四国地方$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 20.14570 21.53441 21.53441 21.53441 21.53441 21.53441 21.53441 21.53441
##  [9] 21.53441 21.53441 21.53441 21.53441 21.53441 21.53441
## 
## $四国地方$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 15.38623 12.86672
## 49.71429 16.14955 13.29897
## 49.85714 15.97200 13.02743
## 50.00000 15.79994 12.76429
## 50.14286 15.63290 12.50882
## 50.28571 15.47045 12.26039
## 50.42857 15.31225 12.01843
## 50.57143 15.15797 11.78248
## 50.71429 15.00733 11.55211
## 50.85714 14.86010 11.32693
## 51.00000 14.71604 11.10661
## 51.14286 14.57497 10.89086
## 51.28571 14.43669 10.67939
## 51.42857 14.30106 10.47196
## 
## $四国地方$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 24.90516 27.42467
## 49.71429 26.91927 29.76985
## 49.85714 27.09683 30.04139
## 50.00000 27.26888 30.30453
## 50.14286 27.43593 30.56000
## 50.28571 27.59837 30.80844
## 50.42857 27.75657 31.05039
## 50.57143 27.91085 31.28634
## 50.71429 28.06149 31.51671
## 50.85714 28.20872 31.74189
## 51.00000 28.35278 31.96221
## 51.14286 28.49386 32.17796
## 51.28571 28.63213 32.38943
## 51.42857 28.76776 32.59686
## 
## 
## $九州地方
## $九州地方$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 210.6797 189.3329 204.7778 235.0057 231.1445 245.7778 235.1957 224.3693
##  [9] 210.6652 222.8508 245.5384 240.1503 251.6400 244.6535
## 
## $九州地方$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 186.4265 173.5876
## 49.71429 159.4256 143.5937
## 49.85714 170.2318 151.9443
## 50.00000 199.1203 180.1238
## 50.14286 193.1395 173.0208
## 50.28571 204.4040 182.5020
## 50.42857 191.0177 167.6312
## 50.57143 174.8283 148.6028
## 50.71429 156.7880 128.2672
## 50.85714 165.0570 134.4628
## 51.00000 185.0854 153.0836
## 51.14286 176.6179 142.9859
## 51.28571 184.8387 149.4762
## 51.42857 174.9739 138.0877
## 
## $九州地方$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 234.9329 247.7718
## 49.71429 219.2402 235.0722
## 49.85714 239.3238 257.6113
## 50.00000 270.8911 289.8876
## 50.14286 269.1495 289.2681
## 50.28571 287.1515 309.0535
## 50.42857 279.3737 302.7601
## 50.57143 273.9104 300.1358
## 50.71429 264.5424 293.0632
## 50.85714 280.6446 311.2387
## 51.00000 305.9913 337.9931
## 51.14286 303.6826 337.3146
## 51.28571 318.4413 353.8037
## 51.42857 314.3331 351.2193

　

都道府県別

x_by_pref %>% 
  dplyr::select(pref, n) %>% 
  split(.$pref) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map(., forecast::auto.arima) %>% 
  purrr::map(., forecast::forecast) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道
## $北海道$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 116.50159  91.62504 100.07984  84.00151 114.85162 111.11786 108.61932
##  [8]  93.73385  73.04636  81.27404  70.89493  92.28742  88.38999  88.19557
## 
## $北海道$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##                80%       95%
## 49.57143 101.05995 92.885640
## 49.71429  73.41051 63.768328
## 49.85714  76.95766 64.717520
## 50.00000  59.59954 46.681915
## 50.14286  87.83581 73.534502
## 50.28571  81.50558 65.829785
## 50.42857  76.77355 59.915421
## 50.57143  57.88614 38.909508
## 50.71429  33.94866 13.251595
## 50.85714  37.99801 15.089058
## 51.00000  25.23065  1.057440
## 51.14286  44.10753 18.602632
## 51.28571  37.36383 10.352221
## 51.42857  34.37464  5.883556
## 
## $北海道$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 131.9432 140.1175
## 49.71429 109.8396 119.4817
## 49.85714 123.2020 135.4422
## 50.00000 108.4035 121.3211
## 50.14286 141.8674 156.1687
## 50.28571 140.7301 156.4059
## 50.42857 140.4651 157.3232
## 50.57143 129.5816 148.5582
## 50.71429 112.1440 132.8411
## 50.85714 124.5501 147.4590
## 51.00000 116.5592 140.7324
## 51.14286 140.4673 165.9722
## 51.28571 139.4161 166.4278
## 51.42857 142.0165 170.5076
## 
## 
## $青森県
## $青森県$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 3.333653 1.556135 2.823368 1.919928 2.564011 2.104830 2.432191 2.198808
##  [9] 2.365192 2.246573 2.331139 2.270850 2.313831 2.283189
## 
## $青森県$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##                 80%        95%
## 49.57143  0.5417812 -0.9361462
## 49.71429 -1.5452395 -3.1870079
## 49.85714 -0.3865122 -2.0857200
## 50.00000 -1.5070661 -3.3212074
## 50.14286 -0.9881319 -2.8685228
## 50.28571 -1.6209308 -3.5932293
## 50.42857 -1.4225950 -3.4631951
## 50.57143 -1.8058458 -3.9257813
## 50.71429 -1.7664893 -3.9536691
## 50.85714 -2.0203072 -4.2790569
## 51.00000 -2.0584597 -4.3821728
## 51.14286 -2.2438816 -4.6338359
## 51.28571 -2.3186504 -4.7709380
## 51.42857 -2.4669043 -4.9814516
## 
## $青森県$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 6.125524 7.603452
## 49.71429 4.657509 6.299277
## 49.85714 6.033248 7.732456
## 50.00000 5.346923 7.161064
## 50.14286 6.116154 7.996545
## 50.28571 5.830590 7.802889
## 50.42857 6.286976 8.327576
## 50.57143 6.203461 8.323397
## 50.71429 6.496873 8.684052
## 50.85714 6.513453 8.772202
## 51.00000 6.720737 9.044450
## 51.14286 6.785581 9.175535
## 51.28571 6.946313 9.398600
## 51.42857 7.033282 9.547829
## 
## 
## $岩手県
## $岩手県$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 2.224963 3.973389 5.750625 5.218791 6.325055 4.341207 4.301148 5.217798
##  [9] 5.620272 5.796987 5.874577 5.908645 5.923603 5.930170
## 
## $岩手県$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##                 80%        95%
## 49.57143 -1.1688162 -2.9653742
## 49.71429  0.1881219 -1.8156775
## 49.85714  1.8545848 -0.2078545
## 50.00000  1.2781257 -0.8079365
## 50.14286  2.3592306  0.2598503
## 50.28571  0.3570108 -1.7520952
## 50.42857  0.3012429 -1.8161790
## 50.57143  1.1116073 -1.0620788
## 50.71429  1.4666238 -0.7321849
## 50.85714  1.6141308 -0.6001393
## 51.00000  1.6691417 -0.5570813
## 51.14286  1.6833062 -0.5534528
## 51.28571  1.6795233 -0.5671566
## 51.42857  1.6678903 -0.5884245
## 
## $岩手県$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##                80%       95%
## 49.57143  5.618741  7.415299
## 49.71429  7.758656  9.762456
## 49.85714  9.646666 11.709105
## 50.00000  9.159456 11.245519
## 50.14286 10.290878 12.390259
## 50.28571  8.325403 10.434509
## 50.42857  8.301053 10.418475
## 50.57143  9.323989 11.497675
## 50.71429  9.773921 11.972729
## 50.85714  9.979843 12.194113
## 51.00000 10.080012 12.306235
## 51.14286 10.133983 12.370742
## 51.28571 10.167682 12.414362
## 51.42857 10.192450 12.448765
## 
## 
## $宮城県
## $宮城県$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 24.86775 38.18183 26.40459 38.68404 30.79274 38.40340 30.93248 35.03945
##  [9] 29.33896 32.84685 29.72681 33.06044 31.17017 33.42734
## 
## $宮城県$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 18.83694 15.64442
## 49.71429 32.03974 28.78832
## 49.85714 20.01027 16.62533
## 50.00000 32.28952 28.90447
## 50.14286 24.04299 20.46988
## 50.28571 31.42857 27.73632
## 50.42857 23.28515 19.23690
## 50.57143 27.10858 22.91023
## 50.71429 21.08793 16.72010
## 50.85714 24.52716 20.12299
## 51.00000 21.32141 16.87186
## 51.14286 24.62805 20.16421
## 51.28571 22.65384 18.14557
## 51.42857 24.84007 20.29424
## 
## $宮城県$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 30.89856 34.09107
## 49.71429 44.32391 47.57534
## 49.85714 32.79891 36.18385
## 50.00000 45.07855 48.46361
## 50.14286 37.54249 41.11559
## 50.28571 45.37823 49.07048
## 50.42857 38.57982 42.62807
## 50.57143 42.97032 47.16867
## 50.71429 37.58998 41.95781
## 50.85714 41.16653 45.57071
## 51.00000 38.13221 42.58176
## 51.14286 41.49284 45.95667
## 51.28571 39.68650 44.19477
## 51.42857 42.01460 46.56043
## 
## 
## $秋田県
## $秋田県$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 0.5009707 0.5382204 0.4917117 0.4894184 0.4843238 0.4834159 0.4827713
##  [8] 0.4825927 0.4825028 0.4824725 0.4824593 0.4824544 0.4824524 0.4824516
## 
## $秋田県$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##                 80%       95%
## 49.57143 -0.8351148 -1.542396
## 49.71429 -0.8123235 -1.527258
## 49.85714 -0.8713898 -1.592972
## 50.00000 -0.8752603 -1.597678
## 50.14286 -0.8812205 -1.604096
## 50.28571 -0.8825545 -1.605656
## 50.42857 -0.8835385 -1.606819
## 50.57143 -0.8840138 -1.607452
## 50.71429 -0.8843872 -1.607975
## 50.85714 -0.8846951 -1.608430
## 51.00000 -0.8849839 -1.608865
## 51.14286 -0.8852633 -1.609289
## 51.28571 -0.8855395 -1.609711
## 51.42857 -0.8858142 -1.610130
## 
## $秋田県$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 1.837056 2.544337
## 49.71429 1.888764 2.603699
## 49.85714 1.854813 2.576396
## 50.00000 1.854097 2.576514
## 50.14286 1.849868 2.572744
## 50.28571 1.849386 2.572487
## 50.42857 1.849081 2.572362
## 50.57143 1.849199 2.572637
## 50.71429 1.849393 2.572981
## 50.85714 1.849640 2.573375
## 51.00000 1.849902 2.573783
## 51.14286 1.850172 2.574198
## 51.28571 1.850444 2.574615
## 51.42857 1.850717 2.575034
## 
## 
## $山形県
## $山形県$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 6.431724 6.128776 6.482319 5.767185 5.416916 5.493726 5.444979 5.487931
##  [9] 5.457179 5.457179 5.457179 5.457179 5.457179 5.457179
## 
## $山形県$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%         95%
## 49.57143 4.521301  3.50998400
## 49.71429 4.140232  3.08755975
## 49.85714 4.290836  3.13073561
## 50.00000 3.390027  2.13163536
## 50.14286 2.867570  1.51802732
## 50.28571 2.783108  1.34819350
## 50.42857 2.582159  1.06667376
## 50.57143 2.416121  0.79000355
## 50.71429 2.233840  0.52750689
## 50.85714 2.078138  0.28938207
## 51.00000 1.929302  0.06175659
## 51.14286 1.786496 -0.15664687
## 51.28571 1.649041 -0.36686588
## 51.42857 1.516378 -0.56975693
## 
## $山形県$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%       95%
## 49.57143 8.342148  9.353465
## 49.71429 8.117321  9.169993
## 49.85714 8.673801  9.833902
## 50.00000 8.144343  9.402734
## 50.14286 7.966262  9.315804
## 50.28571 8.204344  9.639259
## 50.42857 8.307798  9.823283
## 50.57143 8.559741 10.185859
## 50.71429 8.680519 10.386852
## 50.85714 8.836220 10.624976
## 51.00000 8.985057 10.852602
## 51.14286 9.127863 11.071005
## 51.28571 9.265318 11.281224
## 51.42857 9.397981 11.484115
## 
## 
## $福島県
## $福島県$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 13.80451 13.76225 14.09995 14.61134 14.00963 13.60580 14.02381 13.96280
##  [9] 13.96280 13.96280 13.96280 13.96280 13.96280 13.96280
## 
## $福島県$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##                80%      95%
## 49.57143 10.841902 9.273589
## 49.71429 10.679279 9.047255
## 49.85714 10.901156 9.207815
## 50.00000 11.300760 9.548247
## 50.14286 10.590930 8.781178
## 50.28571 10.082284 8.217049
## 50.42857 10.398515 8.479401
## 50.57143 10.173137 8.167011
## 50.71429 10.061818 7.996763
## 50.85714  9.953588 7.831240
## 51.00000  9.848205 7.670069
## 51.14286  9.745453 7.512925
## 51.28571  9.645147 7.359519
## 51.42857  9.547118 7.209597
## 
## $福島県$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 16.76713 18.33544
## 49.71429 16.84521 18.47724
## 49.85714 17.29875 18.99209
## 50.00000 17.92191 19.67443
## 50.14286 17.42834 19.23809
## 50.28571 17.12931 18.99454
## 50.42857 17.64910 19.56822
## 50.57143 17.75246 19.75859
## 50.71429 17.86378 19.92884
## 50.85714 17.97201 20.09436
## 51.00000 18.07740 20.25553
## 51.14286 18.18015 20.41267
## 51.28571 18.28045 20.56608
## 51.42857 18.37848 20.71600
## 
## 
## $茨城県
## $茨城県$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 16.34080 16.35480 18.50836 15.76237 17.07500 14.64704 19.35954 15.97755
##  [9] 18.30289 15.89633 17.41484 18.45011 14.97464 16.83570
## 
## $茨城県$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##                80%         95%
## 49.57143  8.694236  4.64639100
## 49.71429  8.401511  4.19129334
## 49.85714 10.259738  5.89318244
## 50.00000  7.228637  2.71115014
## 50.14286  8.265372  3.60183681
## 50.28571  5.569903  0.76475610
## 50.42857 10.022552  5.07984885
## 50.57143  6.119512  0.90098147
## 50.71429  8.141004  2.76162539
## 50.85714  5.439413 -0.09614163
## 51.00000  6.671001  0.98355627
## 51.14286  7.426814  1.59143259
## 51.28571  3.678791 -2.30086877
## 51.42857  5.273723 -0.84681540
## 
## $茨城県$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 23.98736 28.03521
## 49.71429 24.30809 28.51831
## 49.85714 26.75698 31.12353
## 50.00000 24.29611 28.81359
## 50.14286 25.88463 30.54816
## 50.28571 23.72418 28.52933
## 50.42857 28.69653 33.63923
## 50.57143 25.83559 31.05412
## 50.71429 28.46478 33.84416
## 50.85714 26.35324 31.88879
## 51.00000 28.15868 33.84612
## 51.14286 29.47341 35.30879
## 51.28571 26.27049 32.25015
## 51.42857 28.39767 34.51821
## 
## 
## $栃木県
## $栃木県$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 18.32544 18.88572 22.63918 20.51731 23.06758 20.45174 19.55787 19.09504
##  [9] 19.21893 22.80291 20.91737 22.04372 20.74516 19.46726
## 
## $栃木県$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 14.40941 12.33639
## 49.71429 14.88464 12.76659
## 49.85714 18.55481 16.39267
## 50.00000 16.35132 14.14597
## 50.14286 18.82153 16.57381
## 50.28571 16.12713 13.83782
## 50.42857 15.15609 12.82593
## 50.57143 14.41787 11.94193
## 50.71429 14.44020 11.91049
## 50.85714 17.92473 15.34238
## 51.00000 15.94173 13.30778
## 51.14286 16.97248 14.28794
## 51.28571 15.58011 12.84589
## 51.42857 14.21006 11.42707
## 
## $栃木県$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 22.24147 24.31449
## 49.71429 22.88681 25.00486
## 49.85714 26.72355 28.88568
## 50.00000 24.68330 26.88864
## 50.14286 27.31362 29.56134
## 50.28571 24.77636 27.06567
## 50.42857 23.95965 26.28982
## 50.57143 23.77220 26.24815
## 50.71429 23.99765 26.52736
## 50.85714 27.68109 30.26344
## 51.00000 25.89301 28.52696
## 51.14286 27.11495 29.79950
## 51.28571 25.91021 28.64443
## 51.42857 24.72446 27.50746
## 
## 
## $群馬県
## $群馬県$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 40.16880 33.96451 39.08017 44.20485 45.40926 41.28683 39.32649 39.76626
##  [9] 40.28003 38.99928 42.61460 41.21255 41.13820 39.34113
## 
## $群馬県$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 34.32212 31.22708
## 49.71429 27.12212 23.49997
## 49.85714 31.86258 28.04181
## 50.00000 36.89878 33.03118
## 50.14286 37.80520 33.77986
## 50.28571 33.57383 29.49081
## 50.42857 31.36457 27.14978
## 50.57143 31.21860 26.69374
## 50.71429 31.24736 26.46576
## 50.85714 29.71398 24.79863
## 51.00000 33.08408 28.03893
## 51.14286 31.44237 26.27035
## 51.28571 31.13459 25.83900
## 51.42857 29.10901 23.69246
## 
## $群馬県$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 46.01548 49.11053
## 49.71429 40.80691 44.42906
## 49.85714 46.29777 50.11854
## 50.00000 51.51092 55.37852
## 50.14286 53.01332 57.03866
## 50.28571 48.99984 53.08286
## 50.42857 47.28842 51.50321
## 50.57143 48.31392 52.83878
## 50.71429 49.31270 54.09430
## 50.85714 48.28459 53.19993
## 51.00000 52.14512 57.19028
## 51.14286 50.98273 56.15475
## 51.28571 51.14181 56.43740
## 51.42857 49.57325 54.98980
## 
## 
## $埼玉県
## $埼玉県$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 176.9226 161.5070 189.0695 188.5847 200.5467 199.3978 211.3052 189.8874
##  [9] 186.7556 193.6323 198.9693 200.6484 201.4055 205.5418
## 
## $埼玉県$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 158.7731 149.1653
## 49.71429 142.4569 132.3723
## 49.85714 169.5455 159.2101
## 50.00000 168.5980 158.0177
## 50.14286 180.1079 169.2882
## 50.28571 178.5165 167.4626
## 50.42857 189.9907 178.7075
## 50.57143 165.1975 152.1275
## 50.71429 161.1235 147.5547
## 50.85714 167.2601 153.2995
## 51.00000 171.8772 157.5355
## 51.14286 172.8551 158.1422
## 51.28571 172.9283 157.8534
## 51.42857 176.3966 160.9681
## 
## $埼玉県$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 195.0722 204.6800
## 49.71429 180.5572 190.6417
## 49.85714 208.5936 218.9290
## 50.00000 208.5714 219.1517
## 50.14286 220.9856 231.8053
## 50.28571 220.2791 231.3329
## 50.42857 232.6197 243.9029
## 50.57143 214.5773 227.6473
## 50.71429 212.3878 225.9566
## 50.85714 220.0045 233.9651
## 51.00000 226.0613 240.4030
## 51.14286 228.4417 243.1546
## 51.28571 229.8828 244.9577
## 51.42857 234.6869 250.1155
## 
## 
## $千葉県
## $千葉県$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 127.4593 117.4876 130.1115 132.3942 144.4479 142.8011 137.6134 136.2598
##  [9] 132.5397 137.6654 138.3662 140.1828 142.7263 139.4067
## 
## $千葉県$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%       95%
## 49.57143 113.7932 106.55882
## 49.71429 101.9729  93.75992
## 49.85714 113.7743 105.12591
## 50.00000 115.4917 106.54406
## 50.14286 127.0649 117.86285
## 50.28571 124.9724 115.53448
## 50.42857 119.3572 109.69301
## 50.57143 116.4725 105.99778
## 50.71429 111.8891 100.95730
## 50.85714 116.3451 105.05877
## 51.00000 116.4472 104.84392
## 51.14286 117.6978 105.79504
## 51.28571 119.6951 107.50315
## 51.42857 115.8439 103.37053
## 
## $千葉県$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 141.1254 148.3598
## 49.71429 133.0022 141.2152
## 49.85714 146.4488 155.0972
## 50.00000 149.2968 158.2444
## 50.14286 161.8308 171.0329
## 50.28571 160.6298 170.0678
## 50.42857 155.8695 165.5337
## 50.57143 156.0471 166.5219
## 50.71429 153.1903 164.1221
## 50.85714 158.9857 170.2720
## 51.00000 160.2852 171.8885
## 51.14286 162.6677 174.5705
## 51.28571 165.7574 177.9494
## 51.42857 162.9694 175.4428
## 
## 
## $東京都
## $東京都$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 565.6607 458.4265 544.5569 730.2863 815.4946 744.0541 799.4598 632.9202
##  [9] 526.9308 613.4690 799.3320 884.5841 813.1579 868.5683
## 
## $東京都$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 511.3399 482.5841
## 49.71429 396.0527 363.0340
## 49.85714 478.1115 442.9375
## 50.00000 660.7910 624.0024
## 50.14286 743.3074 705.0937
## 50.28571 669.3424 629.7924
## 50.42857 722.3285 681.4977
## 50.57143 545.6914 499.5153
## 50.71429 434.2501 385.1880
## 50.85714 516.5837 465.2957
## 51.00000 698.6952 645.4213
## 51.14286 780.4167 725.2738
## 51.28571 705.6028 648.6666
## 51.42857 757.7376 699.0674
## 
## $東京都$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%       95%
## 49.57143 619.9816  648.7373
## 49.71429 520.8003  553.8190
## 49.85714 611.0023  646.1764
## 50.00000 799.7816  836.5701
## 50.14286 887.6819  925.8955
## 50.28571 818.7658  858.3157
## 50.42857 876.5911  917.4220
## 50.57143 720.1489  766.3250
## 50.71429 619.6114  668.6736
## 50.85714 710.3543  761.6423
## 51.00000 899.9688  953.2427
## 51.14286 988.7514 1043.8943
## 51.28571 920.7129  977.6491
## 51.42857 979.3990 1038.0692
## 
## 
## $神奈川県
## $神奈川県$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 260.6347 192.7794 254.2415 322.9025 333.3586 338.9175 329.9284 294.4245
##  [9] 220.8217 289.4937 353.9702 365.4901 369.2128 361.1195
## 
## $神奈川県$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 237.5172 225.2795
## 49.71429 166.9231 153.2356
## 49.85714 226.4159 211.6860
## 50.00000 294.8189 279.9523
## 50.14286 304.6312 289.4239
## 50.28571 309.4343 293.8268
## 50.42857 299.2522 283.0132
## 50.57143 260.4914 242.5283
## 50.71429 185.1112 166.2071
## 50.85714 252.3428 232.6762
## 51.00000 315.8639 295.6917
## 51.14286 326.3006 305.5549
## 51.28571 328.8976 307.5561
## 51.42857 319.5874 297.6016
## 
## $神奈川県$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 283.7523 295.9900
## 49.71429 218.6356 232.3231
## 49.85714 282.0670 296.7970
## 50.00000 350.9861 365.8526
## 50.14286 362.0859 377.2932
## 50.28571 368.4007 384.0082
## 50.42857 360.6046 376.8436
## 50.57143 328.3576 346.3208
## 50.71429 256.5323 275.4363
## 50.85714 326.6447 346.3113
## 51.00000 392.0764 412.2486
## 51.14286 404.6797 425.4254
## 51.28571 409.5279 430.8695
## 51.42857 402.6517 424.6375
## 
## 
## $新潟県
## $新潟県$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 5.571588 5.745043 6.112253 5.669756 5.969694 5.698520 5.943688 5.722032
##  [9] 5.922431 5.741250 5.905056 5.756959 5.890853 5.769800
## 
## $新潟県$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%       95%
## 49.57143 2.552931 0.9549507
## 49.71429 2.573011 0.8938384
## 49.85714 2.933646 1.2509929
## 50.00000 2.427662 0.7114015
## 50.14286 2.663044 0.9126096
## 50.28571 2.378453 0.6209157
## 50.42857 2.566878 0.7793028
## 50.57143 2.329261 0.5332367
## 50.71429 2.478939 0.6560650
## 50.85714 2.279646 0.4471828
## 51.00000 2.397436 0.5406148
## 51.14286 2.229452 0.3621030
## 51.28571 2.321056 0.4313198
## 51.42857 2.178684 0.2776627
## 
## $新潟県$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 8.590244 10.18822
## 49.71429 8.917075 10.59625
## 49.85714 9.290860 10.97351
## 50.00000 8.911850 10.62811
## 50.14286 9.276343 11.02678
## 50.28571 9.018588 10.77613
## 50.42857 9.320498 11.10807
## 50.57143 9.114803 10.91083
## 50.71429 9.365923 11.18880
## 50.85714 9.202855 11.03532
## 51.00000 9.412675 11.26950
## 51.14286 9.284466 11.15182
## 51.28571 9.460650 11.35039
## 51.42857 9.360915 11.26194
## 
## 
## $富山県
## $富山県$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 3.558192 3.450876 3.349146 3.252712 3.161297 3.074641 2.992495 2.914625
##  [9] 2.840809 2.770836 2.704504 2.641626 2.582020 2.525518
## 
## $富山県$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##                  80%        95%
## 49.57143  1.13893290 -0.1417457
## 49.71429  0.87460985 -0.4891832
## 49.85714  0.63954284 -0.7948345
## 50.00000  0.42865656 -1.0663079
## 50.14286  0.23821548 -1.3091703
## 50.28571  0.06535243 -1.5276685
## 50.42857 -0.09220419 -1.7251453
## 50.57143 -0.23629910 -1.9042978
## 50.71429 -0.36845992 -2.0673445
## 50.85714 -0.48997190 -2.2161391
## 51.00000 -0.60193040 -2.3522512
## 51.14286 -0.70527903 -2.4770234
## 51.28571 -0.80083804 -2.5916151
## 51.42857 -0.88932585 -2.6970347
## 
## $富山県$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 5.977452 7.258130
## 49.71429 6.027142 7.390935
## 49.85714 6.058750 7.493127
## 50.00000 6.076767 7.571731
## 50.14286 6.084378 7.631764
## 50.28571 6.083929 7.676950
## 50.42857 6.077194 7.710135
## 50.57143 6.065550 7.733549
## 50.71429 6.050079 7.748963
## 50.85714 6.031643 7.757810
## 51.00000 6.010939 7.761260
## 51.14286 5.988531 7.760275
## 51.28571 5.964879 7.755656
## 51.42857 5.940361 7.748070
## 
## 
## $石川県
## $石川県$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 7.470954 7.470954 7.470954 7.470954 7.470954 7.470954 7.470954 7.470954
##  [9] 7.470954 7.470954 7.470954 7.470954 7.470954 7.470954
## 
## $石川県$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%         95%
## 49.57143 3.882099  1.98227378
## 49.71429 3.681391  1.67531833
## 49.85714 3.490792  1.38382207
## 50.00000 3.308912  1.10566081
## 50.14286 3.134654  0.83915638
## 50.28571 2.967134  0.58295561
## 50.42857 2.805625  0.33594846
## 50.57143 2.649522  0.09721097
## 50.71429 2.498318 -0.13403571
## 50.85714 2.351578 -0.35845535
## 51.00000 2.208929 -0.57661911
## 51.14286 2.070045 -0.78902270
## 51.28571 1.934645 -0.99609964
## 51.42857 1.802478 -1.19823163
## 
## $石川県$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 11.05981 12.95963
## 49.71429 11.26052 13.26659
## 49.85714 11.45111 13.55809
## 50.00000 11.63299 13.83625
## 50.14286 11.80725 14.10275
## 50.28571 11.97477 14.35895
## 50.42857 12.13628 14.60596
## 50.57143 12.29238 14.84470
## 50.71429 12.44359 15.07594
## 50.85714 12.59033 15.30036
## 51.00000 12.73298 15.51853
## 51.14286 12.87186 15.73093
## 51.28571 13.00726 15.93801
## 51.42857 13.13943 16.14014
## 
## 
## $福井県
## $福井県$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 0.2179197 0.2480231 0.3507870 0.4387240 0.5139734 0.5783657 0.6334674
##  [8] 0.6806189 0.7209674 0.7554943 0.7850397 0.8103222 0.8319569 0.8504702
## 
## $福井県$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##                80%       95%
## 49.57143 -1.795255 -2.860966
## 49.71429 -1.909934 -3.052287
## 49.85714 -1.997425 -3.240493
## 50.00000 -2.039557 -3.351479
## 50.14286 -2.055378 -3.415511
## 50.28571 -2.055677 -3.450054
## 50.42857 -2.046955 -3.465885
## 50.57143 -2.033263 -3.469905
## 50.71429 -2.017156 -3.466631
## 50.85714 -2.000245 -3.459045
## 51.00000 -1.983527 -3.449118
## 51.14286 -1.967601 -3.438144
## 51.28571 -1.952797 -3.426957
## 51.42857 -1.939275 -3.416077
## 
## $福井県$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 2.231095 3.296805
## 49.71429 2.405980 3.548333
## 49.85714 2.698999 3.942067
## 50.00000 2.917005 4.228927
## 50.14286 3.083325 4.443457
## 50.28571 3.212408 4.606786
## 50.42857 3.313890 4.732820
## 50.57143 3.394501 4.831143
## 50.71429 3.459091 4.908566
## 50.85714 3.511233 4.970034
## 51.00000 3.553607 5.019198
## 51.14286 3.588245 5.058789
## 51.28571 3.616711 5.090871
## 51.42857 3.640216 5.117018
## 
## 
## $山梨県
## $山梨県$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 8.197144 7.418529 5.962221 6.820887 6.520140 6.426671 6.022793 5.022328
##  [9] 5.317411 5.558373 5.445950 5.109703 5.818584 6.443417
## 
## $山梨県$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%       95%
## 49.57143 5.897251 4.6797615
## 49.71429 4.880613 3.5371217
## 49.85714 3.191021 1.7240361
## 50.00000 3.977108 2.4717019
## 50.14286 3.604117 2.0604681
## 50.28571 3.474375 1.9115237
## 50.42857 3.034787 1.4530316
## 50.57143 2.030550 0.4467985
## 50.71429 2.312914 0.7224287
## 50.85714 2.543950 0.9482102
## 51.00000 2.417355 0.8141131
## 51.14286 2.068077 0.4579384
## 51.28571 2.762228 1.1442917
## 51.42857 3.372798 1.7473099
## 
## $山梨県$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##                80%       95%
## 49.57143 10.497036 11.714526
## 49.71429  9.956445 11.299936
## 49.85714  8.733421 10.200406
## 50.00000  9.664667 11.170073
## 50.14286  9.436163 10.979813
## 50.28571  9.378967 10.941818
## 50.42857  9.010799 10.592554
## 50.57143  8.014106  9.597858
## 50.71429  8.321909  9.912393
## 50.85714  8.572797 10.168536
## 51.00000  8.474545 10.077787
## 51.14286  8.151328  9.761467
## 51.28571  8.874939 10.492875
## 51.42857  9.514036 11.139523
## 
## 
## $長野県
## $長野県$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 11.74562 11.36248 11.93603 11.77134 12.45348 12.27983 12.16129 12.13008
##  [9] 12.13008 12.13008 12.13008 12.13008 12.13008 12.13008
## 
## $長野県$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 7.965764 5.964829
## 49.71429 7.269002 5.102048
## 49.85714 7.551316 5.230187
## 50.00000 7.113555 4.647872
## 50.14286 7.537777 4.935558
## 50.28571 7.119077 4.387138
## 50.42857 6.766610 3.910836
## 50.57143 6.596106 3.666595
## 50.71429 6.414072 3.388197
## 50.85714 6.237658 3.118396
## 51.00000 6.066375 2.856441
## 51.14286 5.899799 2.601684
## 51.28571 5.737562 2.353564
## 51.42857 5.579342 2.111588
## 
## $長野県$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 15.52547 17.52641
## 49.71429 15.45595 17.62290
## 49.85714 16.32075 18.64188
## 50.00000 16.42913 18.89481
## 50.14286 17.36919 19.97141
## 50.28571 17.44059 20.17253
## 50.42857 17.55598 20.41175
## 50.57143 17.66406 20.59357
## 50.71429 17.84610 20.87197
## 50.85714 18.02251 21.14177
## 51.00000 18.19379 21.40373
## 51.14286 18.36037 21.65849
## 51.28571 18.52261 21.90661
## 51.42857 18.68083 22.14858
## 
## 
## $岐阜県
## $岐阜県$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 41.07627 33.12343 38.46677 40.78622 41.55761 46.06575 37.90810 40.87668
##  [9] 40.99487 41.11306 41.23124 41.34943 41.46762 41.58581
## 
## $岐阜県$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 35.71059 32.87017
## 49.71429 27.57627 24.63977
## 49.85714 32.74387 29.71435
## 50.00000 34.89283 31.77305
## 50.14286 35.49852 32.29103
## 50.28571 39.84537 36.55250
## 50.42857 31.53052 28.15443
## 50.57143 33.78915 30.03723
## 50.71429 33.67009 29.79258
## 50.85714 33.55847 29.55932
## 51.00000 33.45365 29.33644
## 51.14286 33.35504 29.12307
## 51.28571 33.26216 28.91846
## 51.42857 33.17458 28.72194
## 
## $岐阜県$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 46.44196 49.28238
## 49.71429 38.67060 41.60709
## 49.85714 44.18967 47.21919
## 50.00000 46.67962 49.79939
## 50.14286 47.61670 50.82419
## 50.28571 52.28613 55.57900
## 50.42857 44.28569 47.66178
## 50.57143 47.96422 51.71613
## 50.71429 48.31965 52.19716
## 50.85714 48.66764 52.66679
## 51.00000 49.00884 53.12605
## 51.14286 49.34382 53.57579
## 51.28571 49.67308 54.01678
## 51.42857 49.99704 54.44967
## 
## 
## $静岡県
## $静岡県$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 28.36609 24.15891 20.97490 28.11538 31.09427 23.17082 26.92097 25.48965
##  [9] 22.34810 19.95367 25.32344 27.56362 21.60504 24.42522
## 
## $静岡県$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##                80%        95%
## 49.57143 20.960440 17.0401264
## 49.71429 15.621706 11.1023844
## 49.85714 11.884025  7.0716077
## 50.00000 18.502670 13.4140101
## 50.14286 20.986637 15.6359782
## 50.28571 12.591388  6.9909726
## 50.42857 15.889895 10.0503966
## 50.57143 13.336569  6.9031178
## 50.71429  9.470848  2.6540419
## 50.85714  6.481933 -0.6495722
## 51.00000 11.282371  3.8494792
## 51.14286 12.975425  5.2528992
## 51.28571  6.489504 -1.5121789
## 51.42857  8.800132  0.5287078
## 
## $静岡県$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 35.77174 39.69205
## 49.71429 32.69611 37.21543
## 49.85714 30.06577 34.87819
## 50.00000 37.72808 42.81674
## 50.14286 41.20191 46.55257
## 50.28571 33.75026 39.35067
## 50.42857 37.95205 43.79155
## 50.57143 37.64273 44.07618
## 50.71429 35.22535 42.04216
## 50.85714 33.42540 40.55691
## 51.00000 39.36451 46.79740
## 51.14286 42.15182 49.87435
## 51.28571 36.72058 44.72227
## 51.42857 40.05032 48.32174
## 
## 
## $愛知県
## $愛知県$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 191.7750 133.9745 220.3713 245.3395 240.7582 231.1933 238.8066 195.4337
##  [9] 141.1926 219.0792 245.1910 244.4821 232.7020 238.7991
## 
## $愛知県$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%       95%
## 49.57143 173.0625 163.15674
## 49.71429 112.2589 100.76330
## 49.85714 196.2654 183.50448
## 50.00000 219.2646 205.46137
## 50.14286 213.0267 198.34659
## 50.28571 202.0481 186.61958
## 50.42857 208.4425 192.36876
## 50.57143 158.7577 139.34267
## 50.71429 101.5555  80.57283
## 50.85714 176.9490 154.64652
## 51.00000 200.9320 177.50265
## 51.14286 198.3869 173.98564
## 51.28571 185.0110 159.76498
## 51.42857 189.7131 163.72851
## 
## $愛知県$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 210.4875 220.3933
## 49.71429 155.6901 167.1857
## 49.85714 244.4772 257.2381
## 50.00000 271.4144 285.2176
## 50.14286 268.4896 283.1697
## 50.28571 260.3384 275.7669
## 50.42857 269.1707 285.2444
## 50.57143 232.1096 251.5246
## 50.71429 180.8297 201.8124
## 50.85714 261.2095 283.5120
## 51.00000 289.4501 312.8794
## 51.14286 290.5773 314.9786
## 51.28571 280.3929 305.6389
## 51.42857 287.8851 313.8696
## 
## 
## $三重県
## $三重県$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1]  9.416083 10.798883 10.639862 11.774817 10.585172  9.191914 10.136310
##  [8] 10.344116 11.010995 10.764384 10.962860 10.377797 10.124336 10.556459
## 
## $三重県$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%       95%
## 49.57143 5.091776 2.8026263
## 49.71429 6.079281 3.5808745
## 49.85714 5.640285 2.9936680
## 50.00000 6.568740 3.8128087
## 50.14286 5.221804 2.3826079
## 50.28571 3.705342 0.8009259
## 50.42857 4.550748 1.5939306
## 50.57143 4.385034 1.2304856
## 50.71429 4.895757 1.6585451
## 50.85714 4.522628 1.2184415
## 51.00000 4.616116 1.2563522
## 51.14286 3.941989 0.5350773
## 51.28571 3.611424 0.1636968
## 51.42857 3.975552 0.4918297
## 
## $三重県$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 13.74039 16.02954
## 49.71429 15.51848 18.01689
## 49.85714 15.63944 18.28606
## 50.00000 16.98089 19.73683
## 50.14286 15.94854 18.78774
## 50.28571 14.67849 17.58290
## 50.42857 15.72187 18.67869
## 50.57143 16.30320 19.45775
## 50.71429 17.12623 20.36344
## 50.85714 17.00614 20.31033
## 51.00000 17.30960 20.66937
## 51.14286 16.81361 20.22052
## 51.28571 16.63725 20.08497
## 51.42857 17.13737 20.62109
## 
## 
## $滋賀県
## $滋賀県$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1]  9.359831 11.078664  9.290201  9.774274  9.204225  9.337355  9.154640
##  [8]  9.190003  9.131085  9.139978  9.120859  9.122888  9.116642  9.117014
## 
## $滋賀県$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%       95%
## 49.57143 5.445419 3.3732545
## 49.71429 7.095881 4.9875226
## 49.85714 4.885884 2.5543795
## 50.00000 5.282795 2.9051497
## 50.14286 4.551986 2.0892398
## 50.28571 4.599483 2.0914055
## 50.42857 4.314112 1.7516911
## 50.57143 4.268075 1.6625643
## 50.71429 4.124264 1.4738129
## 50.85714 4.055344 1.3637012
## 51.00000 3.958326 1.2254460
## 51.14286 3.885352 1.1127679
## 51.28571 3.804841 0.9929437
## 51.42857 3.732515 0.8821333
## 
## $滋賀県$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 13.27424 15.34641
## 49.71429 15.06145 17.16980
## 49.85714 13.69452 16.02602
## 50.00000 14.26575 16.64340
## 50.14286 13.85646 16.31921
## 50.28571 14.07523 16.58330
## 50.42857 13.99517 16.55759
## 50.57143 14.11193 16.71744
## 50.71429 14.13791 16.78836
## 50.85714 14.22461 16.91626
## 51.00000 14.28339 17.01627
## 51.14286 14.36042 17.13301
## 51.28571 14.42844 17.24034
## 51.42857 14.50151 17.35190
## 
## 
## $京都府
## $京都府$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 78.25795 74.14880 82.97897 89.87267 85.78855 84.51785 85.84701 84.48787
##  [9] 83.27572 85.80133 88.62032 87.97736 87.49894 87.43519
## 
## $京都府$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 70.06079 65.72148
## 49.71429 64.67691 59.66280
## 49.85714 73.24592 68.09356
## 50.00000 79.70893 74.32857
## 50.14286 75.21163 69.61256
## 50.28571 73.54331 67.73374
## 50.42857 74.48876 68.47606
## 50.57143 72.14940 65.61781
## 50.71429 70.29800 63.42801
## 50.85714 72.37433 65.26650
## 51.00000 74.71828 67.35898
## 51.14286 73.61599 66.01354
## 51.28571 72.69248 64.85441
## 51.42857 72.19663 64.12983
## 
## $京都府$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##                80%       95%
## 49.57143  86.45511  90.79442
## 49.71429  83.62068  88.63480
## 49.85714  92.71201  97.86438
## 50.00000 100.03641 105.41677
## 50.14286  96.36546 101.96454
## 50.28571  95.49238 101.30195
## 50.42857  97.20526 103.21796
## 50.57143  96.82634 103.35793
## 50.71429  96.25344 103.12343
## 50.85714  99.22834 106.33617
## 51.00000 102.52237 109.88167
## 51.14286 102.33874 109.94119
## 51.28571 102.30541 110.14347
## 51.42857 102.67374 110.74055
## 
## 
## $大阪府
## $大阪府$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 266.8794 197.9824 264.9722 342.4848 316.9233 288.7881 302.7017 256.9061
##  [9] 196.7298 250.8142 313.3942 292.7571 270.0420 281.2752
## 
## $大阪府$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 231.3672 212.5682
## 49.71429 157.0121 135.3238
## 49.85714 222.3925 199.8521
## 50.00000 298.3542 274.9929
## 50.14286 271.2946 247.1403
## 50.28571 241.7089 216.7867
## 50.42857 254.2154 228.5483
## 50.57143 200.3858 170.4657
## 50.71429 136.2101 104.1729
## 50.85714 187.8720 154.5525
## 51.00000 248.1194 213.5650
## 51.14286 225.2302 189.4836
## 51.28571 200.3357 163.4354
## 51.42857 209.4556 171.4367
## 
## $大阪府$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 302.3916 321.1906
## 49.71429 238.9526 260.6409
## 49.85714 307.5519 330.0923
## 50.00000 386.6153 409.9766
## 50.14286 362.5520 386.7064
## 50.28571 335.8673 360.7895
## 50.42857 351.1880 376.8551
## 50.57143 313.4264 343.3465
## 50.71429 257.2495 289.2867
## 50.85714 313.7564 347.0759
## 51.00000 378.6690 413.2234
## 51.14286 360.2840 396.0306
## 51.28571 339.7483 376.6486
## 51.42857 353.0948 391.1138
## 
## 
## $兵庫県
## $兵庫県$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 120.02384  89.09809 137.42751 136.62653 154.70839 123.37307 133.53019
##  [8] 122.48562  95.88568 137.45492 136.76597 152.31860 125.36638 134.10275
## 
## $兵庫県$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##                80%       95%
## 49.57143 105.97698  98.54103
## 49.71429  74.17913  66.28151
## 49.85714 121.68469 113.35095
## 50.00000 120.10086 111.35270
## 50.14286 137.43533 128.29152
## 50.28571 105.38364  95.86060
## 50.42857 114.85183 104.96411
## 50.57143 100.69115  89.15385
## 50.71429  72.91825  60.76003
## 50.85714 113.37160 100.62267
## 51.00000 111.61623  98.30275
## 51.14286 126.14584 112.29082
## 51.28571  98.20911  83.83292
## 51.42857 105.99544  91.11633
## 
## $兵庫県$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 134.0707 141.5066
## 49.71429 104.0170 111.9147
## 49.85714 153.1703 161.5041
## 50.00000 153.1522 161.9003
## 50.14286 171.9815 181.1253
## 50.28571 141.3625 150.8855
## 50.42857 152.2086 162.0963
## 50.57143 144.2801 155.8174
## 50.71429 118.8531 131.0113
## 50.85714 161.5382 174.2872
## 51.00000 161.9157 175.2292
## 51.14286 178.4914 192.3464
## 51.28571 152.5236 166.8998
## 51.42857 162.2101 177.0892
## 
## 
## $奈良県
## $奈良県$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 24.05668 24.42693 22.58470 22.99306 23.82550 24.18783 24.44451 24.06282
##  [9] 23.79048 23.66647 23.73931 23.90678 23.98894 23.98371
## 
## $奈良県$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 19.45864 17.02459
## 49.71429 19.52030 16.92289
## 49.85714 17.32378 14.53882
## 50.00000 17.69991 14.89788
## 50.14286 18.51034 15.69667
## 50.28571 18.74775 15.86794
## 50.42857 18.81760 15.83889
## 50.57143 18.17492 15.05806
## 50.71429 17.71018 14.49146
## 50.85714 17.45110 14.16087
## 51.00000 17.41585 14.06842
## 51.14286 17.47467 14.06972
## 51.28571 17.42704 13.95338
## 51.42857 17.27887 13.72955
## 
## $奈良県$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 28.65471 31.08876
## 49.71429 29.33355 31.93096
## 49.85714 27.84561 30.63057
## 50.00000 28.28620 31.08823
## 50.14286 29.14066 31.95434
## 50.28571 29.62791 32.50771
## 50.42857 30.07143 33.05014
## 50.57143 29.95071 33.06757
## 50.71429 29.87077 33.08949
## 50.85714 29.88185 33.17207
## 51.00000 30.06276 33.41019
## 51.14286 30.33889 33.74385
## 51.28571 30.55084 34.02451
## 51.42857 30.68855 34.23788
## 
## 
## $和歌山県
## $和歌山県$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 2.741707 2.980038 2.980038 2.980038 2.980038 2.980038 2.980038 2.980038
##  [9] 2.980038 2.980038 2.980038 2.980038 2.980038 2.980038
## 
## $和歌山県$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##                  80%        95%
## 49.57143  0.45133431 -0.7611156
## 49.71429  0.50954809 -0.7982500
## 49.85714  0.40792194 -0.9536738
## 50.00000  0.31016128 -1.1031859
## 50.14286  0.21585596 -1.2474134
## 50.28571  0.12466360 -1.3868801
## 50.42857  0.03629487 -1.5220284
## 50.57143 -0.04949732 -1.6532362
## 50.71429 -0.13292600 -1.7808294
## 50.85714 -0.21417638 -1.9050911
## 51.00000 -0.29341066 -2.0262695
## 51.14286 -0.37077187 -2.1445832
## 51.28571 -0.44638687 -2.2602264
## 51.42857 -0.52036884 -2.3733721
## 
## $和歌山県$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 5.032079 6.244529
## 49.71429 5.450527 6.758325
## 49.85714 5.552153 6.913749
## 50.00000 5.649914 7.063261
## 50.14286 5.744219 7.207489
## 50.28571 5.835412 7.346955
## 50.42857 5.923780 7.482104
## 50.57143 6.009572 7.613311
## 50.71429 6.093001 7.740905
## 50.85714 6.174252 7.865166
## 51.00000 6.253486 7.986345
## 51.14286 6.330847 8.104658
## 51.28571 6.406462 8.220302
## 51.42857 6.480444 8.333447
## 
## 
## $鳥取県
## $鳥取県$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 0.4326144 0.4288479 0.4260550 0.4239842 0.4224487 0.4213101 0.4204659
##  [8] 0.4198400 0.4193758 0.4190316 0.4187765 0.4185872 0.4184469 0.4183429
## 
## $鳥取県$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##                 80%       95%
## 49.57143 -0.5068612 -1.004190
## 49.71429 -0.5259069 -1.031323
## 49.85714 -0.5378201 -1.048065
## 50.00000 -0.5455094 -1.058728
## 50.14286 -0.5506270 -1.065742
## 50.28571 -0.5541380 -1.070509
## 50.42857 -0.5566202 -1.073858
## 50.57143 -0.5584280 -1.076292
## 50.71429 -0.5597839 -1.078120
## 50.85714 -0.5608314 -1.079540
## 51.00000 -0.5616646 -1.080679
## 51.14286 -0.5623468 -1.081622
## 51.28571 -0.5629215 -1.082427
## 51.42857 -0.5634188 -1.083132
## 
## $鳥取県$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 1.372090 1.869418
## 49.71429 1.383603 1.889019
## 49.85714 1.389930 1.900175
## 50.00000 1.393478 1.906697
## 50.14286 1.395524 1.910640
## 50.28571 1.396758 1.913129
## 50.42857 1.397552 1.914790
## 50.57143 1.398108 1.915972
## 50.71429 1.398536 1.916871
## 50.85714 1.398895 1.917603
## 51.00000 1.399217 1.918232
## 51.14286 1.399521 1.918796
## 51.28571 1.399815 1.919320
## 51.42857 1.400105 1.919818
## 
## 
## $島根県
## $島根県$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 0.5264706 0.5264706 0.5264706 0.5264706 0.5264706 0.5264706 0.5264706
##  [8] 0.5264706 0.5264706 0.5264706 0.5264706 0.5264706 0.5264706 0.5264706
## 
## $島根県$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##                80%      95%
## 49.57143 -5.934656 -9.35497
## 49.71429 -5.934656 -9.35497
## 49.85714 -5.934656 -9.35497
## 50.00000 -5.934656 -9.35497
## 50.14286 -5.934656 -9.35497
## 50.28571 -5.934656 -9.35497
## 50.42857 -5.934656 -9.35497
## 50.57143 -5.934656 -9.35497
## 50.71429 -5.934656 -9.35497
## 50.85714 -5.934656 -9.35497
## 51.00000 -5.934656 -9.35497
## 51.14286 -5.934656 -9.35497
## 51.28571 -5.934656 -9.35497
## 51.42857 -5.934656 -9.35497
## 
## $島根県$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 6.987598 10.40791
## 49.71429 6.987598 10.40791
## 49.85714 6.987598 10.40791
## 50.00000 6.987598 10.40791
## 50.14286 6.987598 10.40791
## 50.28571 6.987598 10.40791
## 50.42857 6.987598 10.40791
## 50.57143 6.987598 10.40791
## 50.71429 6.987598 10.40791
## 50.85714 6.987598 10.40791
## 51.00000 6.987598 10.40791
## 51.14286 6.987598 10.40791
## 51.28571 6.987598 10.40791
## 51.42857 6.987598 10.40791
## 
## 
## $岡山県
## $岡山県$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 45.43421 42.99044 41.46758 44.09215 42.80446 42.30677 50.07470 45.79266
##  [9] 45.79266 45.79266 45.79266 45.79266 45.79266 45.79266
## 
## $岡山県$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 41.05458 38.73614
## 49.71429 38.23742 35.72132
## 49.85714 36.36844 33.66912
## 50.00000 38.66895 35.79807
## 50.14286 37.07548 34.04274
## 50.28571 36.28753 33.10114
## 50.42857 43.77858 40.44561
## 50.57143 38.87119 35.20718
## 50.71429 38.50526 34.64755
## 50.85714 38.15685 34.11470
## 51.00000 37.82366 33.60513
## 51.14286 37.50385 33.11602
## 51.28571 37.19593 32.64510
## 51.42857 36.89866 32.19047
## 
## $岡山県$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 49.81385 52.13229
## 49.71429 47.74346 50.25956
## 49.85714 46.56671 49.26603
## 50.00000 49.51536 52.38624
## 50.14286 48.53343 51.56617
## 50.28571 48.32600 51.51239
## 50.42857 56.37083 59.70379
## 50.57143 52.71414 56.37814
## 50.71429 53.08006 56.93778
## 50.85714 53.42847 57.47063
## 51.00000 53.76166 57.98020
## 51.14286 54.08147 58.46931
## 51.28571 54.38939 58.94023
## 51.42857 54.68666 59.39486
## 
## 
## $広島県
## $広島県$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 105.03299  78.78974 107.16427  92.06409  96.78734 117.57969  86.31067
##  [8] 103.32043 103.12039  81.49214 108.04910  95.73776  90.48064  95.74237
## 
## $広島県$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##                80%       95%
## 49.57143  98.93200  95.70234
## 49.71429  71.64917  67.86918
## 49.85714  99.74380  95.81564
## 50.00000  82.83792  77.95388
## 50.14286  86.86506  81.61253
## 50.28571 106.81482 101.11624
## 50.42857  73.95258  67.41059
## 50.57143  90.14111  83.16440
## 50.71429  88.38820  80.58945
## 50.85714  65.72584  57.37967
## 51.00000  91.45855  82.67604
## 51.14286  77.63305  68.04900
## 51.28571  71.68837  61.74035
## 51.42857  75.84693  65.31492
## 
## $広島県$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##                80%       95%
## 49.57143 111.13397 114.36364
## 49.71429  85.93030  89.71029
## 49.85714 114.58474 118.51290
## 50.00000 101.29026 106.17430
## 50.14286 106.70962 111.96215
## 50.28571 128.34456 134.04314
## 50.42857  98.66877 105.21075
## 50.57143 116.49975 123.47646
## 50.71429 117.85259 125.65134
## 50.85714  97.25844 105.60461
## 51.00000 124.63966 133.42216
## 51.14286 113.84247 123.42653
## 51.28571 109.27290 119.22092
## 51.42857 115.63781 126.16982
## 
## 
## $山口県
## $山口県$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 4.693444 4.363654 4.504296 3.669111 4.484930 4.849051 4.450093 4.089781
##  [9] 4.018565 3.900629 3.743575 3.861141 4.310715 3.904993
## 
## $山口県$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##                80%         95%
## 49.57143 2.0775961  0.69284996
## 49.71429 1.5380325  0.04223894
## 49.85714 1.4842788 -0.11442158
## 50.00000 0.5611961 -1.08403484
## 50.14286 1.2817714 -0.41387840
## 50.28571 1.6043295 -0.11332216
## 50.42857 1.1534790 -0.59164306
## 50.57143 0.6717210 -1.13769087
## 50.71429 0.5357227 -1.30798235
## 50.85714 0.3816207 -1.48122981
## 51.00000 0.1869771 -1.69577206
## 51.14286 0.2833907 -1.61055573
## 51.28571 0.7090329 -1.19758266
## 51.42857 0.2896075 -1.62426171
## 
## $山口県$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 7.309291 8.694037
## 49.71429 7.189275 8.685069
## 49.85714 7.524313 9.123013
## 50.00000 6.777027 8.422258
## 50.14286 7.688089 9.383739
## 50.28571 8.093772 9.811424
## 50.42857 7.746707 9.491829
## 50.57143 7.507842 9.317254
## 50.71429 7.501406 9.345111
## 50.85714 7.419637 9.282488
## 51.00000 7.300173 9.182922
## 51.14286 7.438891 9.332837
## 51.28571 7.912398 9.819014
## 51.42857 7.520378 9.434247
## 
## 
## $徳島県
## $徳島県$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 0.5433535 0.4169723 0.0888832 0.6319233 0.4646876 0.5353963 0.6953207
##  [8] 0.5435433 0.5072937 0.5523231 0.3605614 0.4839396 0.4562424 0.3343573
## 
## $徳島県$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##                80%       95%
## 49.57143 -1.120569 -2.001396
## 49.71429 -1.329617 -2.254205
## 49.85714 -1.675276 -2.609166
## 50.00000 -1.149633 -2.092732
## 50.14286 -1.334098 -2.286317
## 50.28571 -1.280454 -2.241708
## 50.42857 -1.137436 -2.107639
## 50.57143 -1.374996 -2.390610
## 50.71429 -1.442934 -2.475322
## 50.85714 -1.420234 -2.464443
## 51.00000 -1.634076 -2.689973
## 51.14286 -1.532536 -2.599994
## 51.28571 -1.581838 -2.660732
## 51.42857 -1.725100 -2.815312
## 
## $徳島県$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 2.207276 3.088103
## 49.71429 2.163561 3.088150
## 49.85714 1.853043 2.786932
## 50.00000 2.413480 3.356579
## 50.14286 2.263473 3.215692
## 50.28571 2.351247 3.312500
## 50.42857 2.528078 3.498281
## 50.57143 2.462083 3.477697
## 50.71429 2.457521 3.489909
## 50.85714 2.524880 3.569089
## 51.00000 2.355199 3.411096
## 51.14286 2.500415 3.567873
## 51.28571 2.494322 3.573217
## 51.42857 2.393815 3.484026
## 
## 
## $香川県
## $香川県$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 1.523193 1.987766 2.129460 2.172676 2.185857 2.189878 2.191104 2.191478
##  [9] 2.191592 2.191627 2.191637 2.191640 2.191641 2.191642
## 
## $香川県$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##                   80%        95%
## 49.57143 -0.350724075 -1.3427159
## 49.71429 -0.008605555 -1.0654206
## 49.85714  0.106508645 -0.9643770
## 50.00000  0.138165184 -0.9388400
## 50.14286  0.143137988 -0.9382123
## 50.28571  0.139872428 -0.9453347
## 50.42857  0.134098893 -0.9548137
## 50.57143  0.127575092 -0.9649890
## 50.71429  0.120838030 -0.9753528
## 50.85714  0.114051477 -0.9857503
## 51.00000  0.107265254 -0.9961346
## 51.14286  0.100494412 -1.0064914
## 51.28571  0.093743396 -1.0168167
## 51.42857  0.087013417 -1.0271095
## 
## $香川県$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 3.397110 4.389102
## 49.71429 3.984137 5.040952
## 49.85714 4.152411 5.223296
## 50.00000 4.207188 5.284193
## 50.14286 4.228577 5.309927
## 50.28571 4.239883 5.325090
## 50.42857 4.248109 5.337021
## 50.57143 4.255380 5.347944
## 50.71429 4.262346 5.358536
## 50.85714 4.269202 5.369003
## 51.00000 4.276009 5.379409
## 51.14286 4.282786 5.389772
## 51.28571 4.289539 5.400100
## 51.42857 4.296270 5.410393
## 
## 
## $愛媛県
## $愛媛県$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 1.0535861 1.0669118 0.7763316 0.2063609 0.9804367 0.9521234 1.2803442
##  [8] 1.1347407 1.1347407 1.1347407 1.1347407 1.1347407 1.1347407 1.1347407
## 
## $愛媛県$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##                80%       95%
## 49.57143 -1.287603 -2.526953
## 49.71429 -1.696669 -3.159620
## 49.85714 -2.353140 -4.009782
## 50.00000 -3.250488 -5.080433
## 50.14286 -2.775361 -4.763561
## 50.28571 -3.080522 -5.215276
## 50.42857 -3.011328 -5.283201
## 50.57143 -3.297685 -5.644069
## 50.71429 -3.468524 -5.905346
## 50.85714 -3.633247 -6.157267
## 51.00000 -3.792465 -6.400770
## 51.14286 -3.946697 -6.636648
## 51.28571 -4.096384 -6.865574
## 51.42857 -4.241905 -7.088129
## 
## $愛媛県$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 3.394775 4.634125
## 49.71429 3.830493 5.293444
## 49.85714 3.905803 5.562445
## 50.00000 3.663210 5.493155
## 50.14286 4.736235 6.724434
## 50.28571 4.984769 7.119523
## 50.42857 5.572016 7.843890
## 50.57143 5.567166 7.913551
## 50.71429 5.738006 8.174827
## 50.85714 5.902728 8.426748
## 51.00000 6.061946 8.670252
## 51.14286 6.216178 8.906129
## 51.28571 6.365865 9.135055
## 51.42857 6.511386 9.357610
## 
## 
## $高知県
## $高知県$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 17.38274 15.42221 20.02086 18.24161 17.76205 15.67392 15.92777 16.73397
##  [9] 16.66396 16.71343 16.67848 16.70317 16.68572 16.69805
## 
## $高知県$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%       95%
## 49.57143 14.46348 12.918124
## 49.71429 12.17910 10.462303
## 49.85714 16.65578 14.874409
## 50.00000 14.64780 12.745347
## 50.14286 14.03049 12.055116
## 50.28571 11.75835  9.685575
## 50.42857 11.87183  9.724741
## 50.57143 12.36803 10.056838
## 50.71429 12.10556  9.692495
## 50.85714 11.98050  9.475038
## 51.00000 11.77034  9.172136
## 51.14286 11.63060  8.945336
## 51.28571 11.45064  8.679357
## 51.42857 11.30758  8.454034
## 
## $高知県$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 20.30199 21.84735
## 49.71429 18.66532 20.38212
## 49.85714 23.38594 25.16731
## 50.00000 21.83542 23.73787
## 50.14286 21.49362 23.46899
## 50.28571 19.58949 21.66226
## 50.42857 19.98372 22.13080
## 50.57143 21.09991 23.41110
## 50.71429 21.22236 23.63543
## 50.85714 21.44636 23.95182
## 51.00000 21.58661 24.18482
## 51.14286 21.77575 24.46101
## 51.28571 21.92080 24.69209
## 51.42857 22.08853 24.94208
## 
## 
## $福岡県
## $福岡県$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 124.4108 122.7180 129.3188 135.4244 138.6898 154.8651 148.2604 144.3368
##  [9] 143.5730 149.0954 161.1288 157.3597 170.6298 164.5015
## 
## $福岡県$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%       95%
## 49.57143 109.3183 101.32884
## 49.71429 104.9476  95.54046
## 49.85714 110.4618 100.47953
## 50.00000 115.8229 105.44645
## 50.14286 118.2540 107.43592
## 50.28571 133.2933 121.87396
## 50.42857 125.1788 112.96019
## 50.57143 118.7153 105.15212
## 50.71429 115.5762 100.75555
## 50.85714 118.8606 102.85522
## 51.00000 128.7793 111.65448
## 51.14286 123.0096 104.82574
## 51.28571 134.3847 115.19774
## 51.42857 126.4578 106.31869
## 
## $福岡県$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 139.5032 147.4927
## 49.71429 140.4885 149.8956
## 49.85714 148.1758 158.1581
## 50.00000 155.0260 165.4024
## 50.14286 159.1255 169.9436
## 50.28571 176.4368 187.8562
## 50.42857 171.3419 183.5605
## 50.57143 169.9583 183.5215
## 50.71429 171.5697 186.3904
## 50.85714 179.3302 195.3355
## 51.00000 193.4784 210.6032
## 51.14286 191.7098 209.8936
## 51.28571 206.8748 226.0618
## 51.42857 202.5452 222.6843
## 
## 
## $佐賀県
## $佐賀県$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 4.57073 4.57073 4.57073 4.57073 4.57073 4.57073 4.57073 4.57073 4.57073
## [10] 4.57073 4.57073 4.57073 4.57073 4.57073
## 
## $佐賀県$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%          95%
## 49.57143 2.313634  1.118799648
## 49.71429 2.244164  1.012554843
## 49.85714 2.176709  0.909391756
## 50.00000 2.111104  0.809056832
## 50.14286 2.047203  0.711329489
## 50.28571 1.984882  0.616016409
## 50.42857 1.924027  0.522947043
## 50.57143 1.864540  0.431970023
## 50.71429 1.806333  0.342950273
## 50.85714 1.749327  0.255766652
## 51.00000 1.693450  0.170310024
## 51.14286 1.638637  0.086481653
## 51.28571 1.584831  0.004191869
## 51.42857 1.531977 -0.076641060
## 
## $佐賀県$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 6.827825 8.022659
## 49.71429 6.897295 8.128904
## 49.85714 6.964750 8.232067
## 50.00000 7.030355 8.332402
## 50.14286 7.094256 8.430130
## 50.28571 7.156577 8.525443
## 50.42857 7.217432 8.618512
## 50.57143 7.276919 8.709489
## 50.71429 7.335126 8.798509
## 50.85714 7.392132 8.885692
## 51.00000 7.448009 8.971149
## 51.14286 7.502822 9.054977
## 51.28571 7.556628 9.137267
## 51.42857 7.609482 9.218100
## 
## 
## $長崎県
## $長崎県$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 13.372264  9.862483 11.359986 10.589296 11.980919 12.673400 11.843924
##  [8] 12.708837 11.740301 12.198180 11.799647 11.657620 13.092710 11.665998
## 
## $長崎県$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##                80%      95%
## 49.57143 10.959771 9.682674
## 49.71429  7.278408 5.910482
## 49.85714  8.339448 6.740472
## 50.00000  7.304778 5.566060
## 50.14286  8.407451 6.515771
## 50.28571  8.850869 6.827344
## 50.42857  7.780295 5.629139
## 50.57143  8.317795 5.993318
## 50.71429  7.104472 4.650413
## 50.85714  7.306083 4.716364
## 51.00000  6.673899 3.960493
## 51.14286  6.304390 3.470562
## 51.28571  7.522944 4.574488
## 51.42857  5.887116 2.827960
## 
## $長崎県$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 15.78476 17.06185
## 49.71429 12.44656 13.81448
## 49.85714 14.38052 15.97950
## 50.00000 13.87381 15.61253
## 50.14286 15.55439 17.44607
## 50.28571 16.49593 18.51946
## 50.42857 15.90755 18.05871
## 50.57143 17.09988 19.42436
## 50.71429 16.37613 18.83019
## 50.85714 17.09028 19.68000
## 51.00000 16.92539 19.63880
## 51.14286 17.01085 19.84468
## 51.28571 18.66248 21.61093
## 51.42857 17.44488 20.50404
## 
## 
## $熊本県
## $熊本県$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 28.27187 30.46265 28.81160 30.05589 29.11815 29.82486 29.29226 29.69365
##  [9] 29.39115 29.61912 29.44731 29.57679 29.47921 29.55275
## 
## $熊本県$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 23.34238 20.73287
## 49.71429 24.59705 21.49199
## 49.85714 22.67998 19.43410
## 50.00000 23.30023 19.72400
## 50.14286 22.05976 18.32327
## 50.28571 22.28855 18.29907
## 50.42857 21.44368 17.28890
## 50.57143 21.44726 17.08189
## 50.71429 20.83664 16.30816
## 50.85714 20.71585 16.00274
## 51.00000 20.24608 15.37523
## 51.14286 20.05963 15.02154
## 51.28571 19.67632 14.48698
## 51.42857 19.45759 14.11354
## 
## $熊本県$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 33.20136 35.81087
## 49.71429 36.32825 39.43331
## 49.85714 34.94323 38.18911
## 50.00000 36.81154 40.38777
## 50.14286 36.17655 39.91304
## 50.28571 37.36117 41.35065
## 50.42857 37.14084 41.29562
## 50.57143 37.94003 42.30541
## 50.71429 37.94566 42.47414
## 50.85714 38.52239 43.23550
## 51.00000 38.64855 43.51939
## 51.14286 39.09396 44.13205
## 51.28571 39.28211 44.47145
## 51.42857 39.64791 44.99197
## 
## 
## $大分県
## $大分県$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1]  9.696192 10.139120  9.386917 11.033573 11.028851 12.564345 11.190391
##  [8]  9.882812  9.339841 10.015931  9.798082 10.309165  9.123497  9.420105
## 
## $大分県$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 7.120060 5.756338
## 49.71429 7.062083 5.433198
## 49.85714 6.046602 4.278346
## 50.00000 7.505475 5.637813
## 50.14286 7.343424 5.392476
## 50.28571 8.736300 6.709856
## 50.42857 7.228209 5.130757
## 50.57143 5.868564 3.743549
## 50.71429 5.252192 3.088321
## 50.85714 5.845766 3.638214
## 51.00000 5.542419 3.289608
## 51.14286 5.967726 3.669507
## 51.28571 4.697112 2.353925
## 51.42857 4.910022 2.522529
## 
## $大分県$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 12.27232 13.63605
## 49.71429 13.21616 14.84504
## 49.85714 12.72723 14.49549
## 50.00000 14.56167 16.42933
## 50.14286 14.71428 16.66523
## 50.28571 16.39239 18.41883
## 50.42857 15.15257 17.25002
## 50.57143 13.89706 16.02208
## 50.71429 13.42749 15.59136
## 50.85714 14.18610 16.39365
## 51.00000 14.05374 16.30656
## 51.14286 14.65060 16.94882
## 51.28571 13.54988 15.89307
## 51.42857 13.93019 16.31768
## 
## 
## $宮崎県
## $宮崎県$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 10.59234 10.76192 10.76192 10.76192 10.76192 10.76192 10.76192 10.76192
##  [9] 10.76192 10.76192 10.76192 10.76192 10.76192 10.76192
## 
## $宮崎県$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 7.472187 5.820478
## 49.71429 7.465039 5.719776
## 49.85714 7.162104 5.256478
## 50.00000 6.882755 4.829250
## 50.14286 6.622213 4.430786
## 50.28571 6.377126 4.055957
## 50.42857 6.145030 3.700998
## 50.57143 5.924057 3.363048
## 50.71429 5.712745 3.039875
## 50.85714 5.509929 2.729693
## 51.00000 5.314658 2.431053
## 51.14286 5.126149 2.142754
## 51.28571 4.943745 1.863790
## 51.42857 4.766888 1.593311
## 
## $宮崎県$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 13.71249 15.36420
## 49.71429 14.05880 15.80406
## 49.85714 14.36173 16.26736
## 50.00000 14.64108 16.69459
## 50.14286 14.90162 17.09305
## 50.28571 15.14671 17.46788
## 50.42857 15.37881 17.82284
## 50.57143 15.59978 18.16079
## 50.71429 15.81109 18.48396
## 50.85714 16.01391 18.79414
## 51.00000 16.20918 19.09278
## 51.14286 16.39769 19.38108
## 51.28571 16.58009 19.66005
## 51.42857 16.75695 19.93052
## 
## 
## $鹿児島県
## $鹿児島県$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 8.131038 9.377901 9.853473 9.906453 9.933213 9.933213 9.933213 9.933213
##  [9] 9.933213 9.933213 9.933213 9.933213 9.933213 9.933213
## 
## $鹿児島県$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%         95%
## 49.57143 3.568896  1.15384388
## 49.71429 3.831674  0.89567873
## 49.85714 3.916303  0.77335494
## 50.00000 3.730760  0.46154511
## 50.14286 3.681669  0.37230113
## 50.28571 3.649657  0.32334271
## 50.42857 3.617807  0.27463244
## 50.57143 3.586117  0.22616660
## 50.71429 3.554584  0.17794155
## 50.85714 3.523206  0.12995373
## 51.00000 3.491982  0.08219967
## 51.14286 3.460908  0.03467599
## 51.28571 3.429982 -0.01262062
## 51.42857 3.399203 -0.05969337
## 
## $鹿児島県$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 12.69318 15.10823
## 49.71429 14.92413 17.86012
## 49.85714 15.79064 18.93359
## 50.00000 16.08215 19.35136
## 50.14286 16.18476 19.49413
## 50.28571 16.21677 19.54308
## 50.42857 16.24862 19.59179
## 50.57143 16.28031 19.64026
## 50.71429 16.31184 19.68848
## 50.85714 16.34322 19.73647
## 51.00000 16.37444 19.78423
## 51.14286 16.40552 19.83175
## 51.28571 16.43644 19.87905
## 51.42857 16.46722 19.92612
## 
## 
## $沖縄県
## $沖縄県$mean
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##  [1] 18.87943 20.29331 19.61097 22.85442 17.73403 22.43701 17.72870 19.68730
##  [9] 19.68730 19.68730 19.68730 19.68730 19.68730 19.68730
## 
## $沖縄県$lower
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##                 80%         95%
## 49.57143  6.1067829  -0.6546495
## 49.71429  6.5278289  -0.7591744
## 49.85714  4.9196117  -2.8575256
## 50.00000  7.2921620  -0.9459999
## 50.14286  1.3470958  -7.3276238
## 50.28571  5.2649611  -3.8253748
## 50.42857 -0.1941073  -9.6818707
## 50.57143 -0.1493624 -10.6502579
## 50.71429 -1.1829614 -12.2310112
## 50.85714 -2.1677326 -13.7370887
## 51.00000 -3.1100044 -15.1781691
## 51.14286 -4.0148460 -16.5620049
## 51.28571 -4.8863924 -17.8949202
## 51.42857 -5.7280693 -19.1821539
## 
## $沖縄県$upper
## Time Series:
## Start = c(49, 5) 
## End = c(51, 4) 
## Frequency = 7 
##               80%      95%
## 49.57143 31.65208 38.41352
## 49.71429 34.05878 41.34579
## 49.85714 34.30234 42.07947
## 50.00000 38.41668 46.65484
## 50.14286 34.12097 42.79569
## 50.28571 39.60907 48.69940
## 50.42857 35.65152 45.13928
## 50.57143 39.52397 50.02486
## 50.71429 40.55756 51.60561
## 50.85714 41.54234 53.11169
## 51.00000 42.48461 54.55277
## 51.14286 43.38945 55.93661
## 51.28571 44.26100 57.26952
## 51.42857 45.10267 58.55676